{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "* 委案金额分布\n",
    "* 还款金额分布\n",
    "\n",
    "* 年龄和还款j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns \n",
    "\n",
    "df = pd.read_excel('C:\\\\Users\\\\Voyager\\\\Desktop\\\\test.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pin</th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>mobile</th>\n",
       "      <th>real_mobile</th>\n",
       "      <th>latest_mobile</th>\n",
       "      <th>id</th>\n",
       "      <th>email</th>\n",
       "      <th>addr</th>\n",
       "      <th>origin_addr</th>\n",
       "      <th>receive_addr</th>\n",
       "      <th>category</th>\n",
       "      <th>default_days</th>\n",
       "      <th>default_date</th>\n",
       "      <th>repayment_amount</th>\n",
       "      <th>default_amount</th>\n",
       "      <th>assign_com</th>\n",
       "      <th>assign_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>bill_date</th>\n",
       "      <th>last_repayment_date</th>\n",
       "      <th>last_not_pay_amount</th>\n",
       "      <th>unpaid_captical</th>\n",
       "      <th>lowest_pay_amount</th>\n",
       "      <th>bill_flag</th>\n",
       "      <th>is_staff</th>\n",
       "      <th>is_student</th>\n",
       "      <th>is_sensitive</th>\n",
       "      <th>contactor1_name</th>\n",
       "      <th>contactor1_tel</th>\n",
       "      <th>contactor1_relation</th>\n",
       "      <th>contactor2_name</th>\n",
       "      <th>contactor2_tel</th>\n",
       "      <th>contactor2_relation</th>\n",
       "      <th>contactor3_name</th>\n",
       "      <th>contactor3_tel</th>\n",
       "      <th>contactor3_relation</th>\n",
       "      <th>repayment</th>\n",
       "      <th>Unnamed: 38</th>\n",
       "      <th>Unnamed: 39</th>\n",
       "      <th>Unnamed: 40</th>\n",
       "      <th>Unnamed: 41</th>\n",
       "      <th>Unnamed: 42</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-范儿丶</td>\n",
       "      <td>JDBT</td>\n",
       "      <td>赵永涛</td>\n",
       "      <td>15148199181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150428198906052032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>内蒙古赤峰市樱桃花园小区19号楼4单元</td>\n",
       "      <td>内蒙古赤峰市喀喇沁旗西桥镇仕牛营子村八组</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B3</td>\n",
       "      <td>698</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>7012.1</td>\n",
       "      <td>6841.56</td>\n",
       "      <td>上海律正</td>\n",
       "      <td>2019-11-12 00:00:00.0</td>\n",
       "      <td>2020-03-11 00:00:00.0</td>\n",
       "      <td>20191105.0</td>\n",
       "      <td>20191114.0</td>\n",
       "      <td>6841.56</td>\n",
       "      <td>4461.73</td>\n",
       "      <td>6977.62</td>\n",
       "      <td>1</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pin  type name       mobile  real_mobile  latest_mobile  \\\n",
       "0  -范儿丶  JDBT  赵永涛  15148199181          NaN            NaN   \n",
       "\n",
       "                   id email                 addr           origin_addr  \\\n",
       "0  150428198906052032   NaN  内蒙古赤峰市樱桃花园小区19号楼4单元  内蒙古赤峰市喀喇沁旗西桥镇仕牛营子村八组   \n",
       "\n",
       "  receive_addr category  default_days default_date  repayment_amount  \\\n",
       "0          NaN       B3           698   2017-12-15            7012.1   \n",
       "\n",
       "   default_amount assign_com            assign_date               end_date  \\\n",
       "0         6841.56       上海律正  2019-11-12 00:00:00.0  2020-03-11 00:00:00.0   \n",
       "\n",
       "    bill_date  last_repayment_date  last_not_pay_amount  unpaid_captical  \\\n",
       "0  20191105.0           20191114.0              6841.56          4461.73   \n",
       "\n",
       "   lowest_pay_amount  bill_flag is_staff is_student is_sensitive  \\\n",
       "0            6977.62          1        否          否            否   \n",
       "\n",
       "  contactor1_name  contactor1_tel contactor1_relation contactor2_name  \\\n",
       "0             NaN             NaN                 NaN             NaN   \n",
       "\n",
       "   contactor2_tel contactor2_relation contactor3_name  contactor3_tel  \\\n",
       "0             NaN                 NaN             NaN             NaN   \n",
       "\n",
       "  contactor3_relation  repayment  Unnamed: 38 Unnamed: 39  Unnamed: 40  \\\n",
       "0                 NaN        0.0          NaN         NaN          NaN   \n",
       "\n",
       "   Unnamed: 41  Unnamed: 42  \n",
       "0          NaN          NaN  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 100)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "figure, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.distplot(df['default_amount'])\n",
    "\n",
    "# 设置标题\n",
    "plt.title('委案金额分布图', size=30)\n",
    "# 设置图例并设置图例的字体及大小\n",
    "# font1 = {\n",
    "#     'family': 'Times New Roman',\n",
    "#     'weight': 'normal',\n",
    "#     'size': 23\n",
    "# }\n",
    "# legend = plt.legend(handles=[A, B], prop=font1)\n",
    "\n",
    "# 设置坐标刻度值的大小以及刻度值的字体\n",
    "plt.xticks([0,5000,10000, 20000, 30000, 40000, 50000], fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "\n",
    "# 设置横纵坐标的名称以及对应字体格式\n",
    "font2 = {\n",
    "#     'family': 'Times New Roman',\n",
    "    'weight': 'normal',\n",
    "    'size': 25,\n",
    "}\n",
    "plt.xlabel('委案金额', font2)\n",
    "plt.ylabel('', font2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.kdeplot(df[df['repayment']!=0]['default_amount'])\n",
    "# sns.kdeplot(df[df['repayment']!=0]['repayment'])\n",
    "# sns.kdeplot(df['repayment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1000]          87\n",
       "(1000, 5000]       61\n",
       "(5000, 10000]       9\n",
       "(10000, 20000]      7\n",
       "(20000, 100000]     2\n",
       "Name: repayment_segment, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sns.lineplot(df['repayment_segment'][df['repayment_segment'].notnull()].value_counts().index, df['repayment_segment'][df['repayment_segment'].notnull()].value_counts().values)\n",
    "# df['repayment_segment'] = pd.cut(df['repayment'], bins=[0, 1000, 5000, 10000, 20000, 100000])\n",
    "# sns.lineplot(df['repayment_segment'][df['repayment_segment'].notnull()].value_counts().index, df['repayment_segment'][df['repayment_segment'].notnull()].value_counts().values)\n",
    "df['repayment_segment'][df['repayment_segment'].notnull()].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number, not 'pandas._libs.interval.Interval'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-46f9cf28aafe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'委案金额段占比'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'amount_segment'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'amount_segment'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'repayment_segment'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'repayment_segment'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'repayment'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m!=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;31m# for index, row in df['amount_segment'].value_counts().values/len(df).iterrows()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[1;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[0;32m   1082\u001b[0m         \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1083\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1084\u001b[1;33m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1085\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1086\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m    793\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munits\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m                 \u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1853\u001b[0m                         \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1854\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1855\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1856\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1857\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1527\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1528\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1529\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1530\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36madd_line\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m   1930\u001b[0m             \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1931\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1932\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1933\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1934\u001b[0m             \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_line%d'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_update_line_limits\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m   1952\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1953\u001b[0m         \"\"\"\n\u001b[1;32m-> 1954\u001b[1;33m         \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1955\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1956\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36mget_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    949\u001b[0m         \"\"\"\n\u001b[0;32m    950\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 951\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    952\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    953\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36mrecache\u001b[1;34m(self, always)\u001b[0m\n\u001b[0;32m    650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0malways\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    651\u001b[0m             \u001b[0mxconv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xorig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 652\u001b[1;33m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxconv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    653\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    654\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_x\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36m_to_unmasked_float_array\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   2048\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2049\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2050\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2052\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \"\"\"\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'pandas._libs.interval.Interval'"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAF7CAYAAAA64/aAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3X28ZFdZJ/rfQ3dC3hACNBFQDBkzeuciUWyUDCCtEEYRHSdqwh0vyPgSRXRwgFEcMioYUFAjiKA3yhUEdQyvvlyQiBBAJgwkQsCIDo4iGM01GCCEiEB85o+9D1051Omu7j6nz0mv7/fzqU/X2bVq16pau/r8evWz167uDgAAjOx2290BAADYbkIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYOOZU1ecexnN2b3IfvnAz9wfA1irrFAM7WVXdPsm/SLJ74XZykjvMt7skuUeSuyc5Pcl9ktw5yWO7+8Urvsa+JL+a5Gu7+883oc/3SfLOJL/R3d9+GM+/X5L/J8nLu/tZC9tPTHL7JJ9I8ul1TzsuyXHdfeMB9nv/JKd398vmnz8/02e56Jbuft8h9vc7k/yHJJd2988fwvMuSvJF3f2tGzz+niQf6e4HH0p/AA7Hps6MAGyBTvL6TKF30T9m+t+uXUmuTnJ9kr9O8sdJPprkjlW1q7tvWXtCVdXcfr13ZQqVP1VVSwNaprB40FmEqjouyS/Pr/PwqvqX3f0/D/a8df5nkv8zyY1JnrWw/fFJfvoAz3tTkn0LfTk7ySOSfFGSs5N8XpKPVtUbuvsfMv1D4KHr9nFDpn9oHIo9SR6Y5C2H+LwvTfKVB3j8xEzjDLDlhGJgR+vuT84zuZ0p7H48yc3d3VX1S0ke2d17V9zdBUl+6QCP3zPJpzZ47ElJLl7hNX4hU9D7d0m+J8kbq+qh3f1nB3pSVb0mydet2/w1VbUYxM9Kck2moHiPJL+e5BlJXpfkhCQ3rXv+viQXJnl2kt+en/un3f3JhTaXdfe/mfvwU0m+bYX3uN7arPU/HeLzPpWNP+8k+eRG+6yqE7tbYAY2jVAM7FhVdZckZ2QKRp/MVBZx5/mxJLlTkt1V9cVLnr4rU1D804XwtBbA7tDdN837uWeS5yV5Qnd/cIN+dD67XGFZu6dnCt4/2N2/XVWvT/LaJG+pqvO6+40HePrHk7wjyWMylYG8f+GxRyT52fm9vHt+ra+fH3tRd//FBvtcC5Q/2d0fOcDrLrplaavV7K2q793gscpU+nFpd//twmv98wH298n1/amq4zOVlty9qr5+8X8CAI6EUAzsZF+d5GUrtHvvAR77P5KszdJ+LMkLkty7qj6VqfzixUlOTfKFVbW+vnbN0zOVZixVVbfLVNbwxCRP6e7nJkl3f7yqHpHklUkuq6qfTnJRd9+8ZDcnJbk5U73w7yR5VXefv7D/k5IcX1UnzO33Jrk2yXVVdcr0cr0+4K7NCNdc1nFCklO6++/m7fdM8u51zzmSE7C/fr4dyNuS/O1B2ixVVfdN8qIkX5bkA0n+VZL3HM6+ANYTioGd7HeS3DHzTPFcMnFikt9I8jVJ3pepjODyJN+Q5Bu7+40LtcMn5NY1qd+b5P5Jzp9/vsv8+M357PB9l+z/7/vjk7whUwnCrVTVniQvSXJOksd1963KM7r7pnlW9/lJfiTJo6vq4iS/vDZbPfumTLOpr8wUTH91YRWNG5L8VKbyiSvXdeFj8583ZTrxcJkbFu5fm+TzqurhSb547tOaf54fe16mUpV3dffLN9jnMk/r7h9f9sAc7G+fA5RLzCuAHJ/k+MWZ7aq6e6YykO/JNFYXJvnZ7v7EIfQN4ICEYmDHmmtfP1P/WlUPzjTTe7dM9bLflykUPzZTYH1NVf1Ykp/r7k9lXY1tdz90YV9PzTSz+4D1qy1U1Y8k+bEk+7r7bcv6Ns+8fkeSZyY5Jclbk5xQVY/LVP+86HaZZqvfleSumWqTf6KqfifJd3f3x7v7U1X1n7J/pvW1C8//YJIvzDSLnCSXZPoHwZoHZzqRbiNnZvosdmcqN7ljkpcm+ZMkv7vQ7o2Zaqe/f/75Ow+wz0PS3f+c5SfN3XNd3XSSz3y+yXQy3l9m+kyfm6kU5EOb1S+ANUIxsKPNs4SPyBTQzs40k3pOd183zyweN5+M9w2Z6m6fleS7q+pXMtWv/tWSfX51kv+SaRb456vqmiR/Ot8emSkYnneAQLw7yf/I9N/4f5rp5LTXJvmJTLXH/5yp3nkxmB+XqQTi5Ewh/vuS3LRW8lBVj0nyM5lWhPjF7n5HVT0wU1D9j/N7XKsRftn8ul+YqXzg00m+/AAf44fW1xRX1b9P8sHFmtzu/oM5MJ8yb9pwebdNdGP2r6hxXOaZ4uyvNd6d5OeSPKe7//4o9AcYlFAM7FhV9QNJ1ta9/cNMYfj1VXVCVZ2a5M1J/iFJuvvTSZ5QVb+R5KIkP5lpibWXJ3nUuvD3xqq6Q6YZ1LOSfEWmQHrq3OQvk3xxVf1Vd39WzWp3f3ru21cnefY8o32rJeOq6i+S/FF3P3bJW7skySVVtWtue0Km0oBXZ1pJ4u1V9YeZ6qFf1N2vXrKPByX5rSQP2+DjW/TNVfWJTH/n39LdL03yhCS3m4P2spPdvmfdKhWpqjtlqmVe78z5zzOqaqP+/OEGS9p9rLsvWvaE+WTKK7v7v2ywT4BNIxQDO1Z3P2+uD/79dWv9/t+Zlj57Rnc/ed1z/keSc+YLU5yb5LfWAnFVfUGmmeBPZaq/vVum9YDPzlSr+oJMM74PTfKoJM+qqg8k+b0kL+3uKxZe562ZSiaO5P3dMv/5iap6SJLbzTPC98lUN5tMJwV+aXe/a93T1/7+/kCSLznIS12S/SfQXZupdOLtSX44yX9P8pqFtk9Isntex3i9f5nkDw7wOo+eb+t9vLtPWbIdYMcQioEdaV5668syXRDiuHXLrj0uU+nDmzZYji2ZTrT7o9z6im0fTPJvM10U4kNJ/lem2dkfyrTU266FsPv0qrp3ppD3mEyrNFyRdeaZ0fvO/VlcHuxzknzRuiXKdmU62ex93b1Yy7s2+3ynqnpWkh/IFPrfk+RHk1w1l4M8f+EpJ81/rlJScJfu/shc9nH7+fV+oqq+MtNqFM+Z38udM61p/Ksb7GetfOOVmQL1KtbWUAbY0YRiYKe6W6ZwurYCxFrgPClTqP1wpnC25uRM4euGTCdl7Z5//o1MJ8QlU93tE7N8zeELk5xUVRes2/6yTKtgnFRVJy9Z9uwRmUL6J3LrUHxqpmB85sK2tVD6siyc4DaXclw476cz1U+/bn749Zku0HFKFk46zHx1uu7+6FxmcNCl1OYSk8X3/ookL6yqM+eTDb81U13v/7vBLtbe38cOsDbyrcxL3y27iuCBnlOrXD0QYDMJxcCO1N1/k3V/R811xFcl+askD1sLTvOs8jVJ/nLt6mwbeHOmYLm4LFhlviDI7L3ZH6zXHr/9fNub6ZLSi/18YlU9Kcm3dvelC329VU3xvJTckzItJbZ+FYabM5VxXJ2pVvhF6x7//Uzh+z4L2+6fqQRize03eM9Jcq+qemim2un7Z1o67s8z1ST/bJKnVtV3J3lykivmEpRtMS9f94yq+tfb1QdgTEIxcFtycZJ7Zyp9eHpV/XGmZc5+KtNSZ+cc6MndfeLiz3NQfWmSh2Sqzf34vJ/3JnnMunWED+SCJL9UVSd194s2aPNNmVanODHJU9f165Ykj5xrid+T5My1mdiqujzJP81rNP9DkqdlmpV+WJLHVdVZSd6ZaTWNtfd1WpLnZArAyRS2O9NV8q7JvG5xd99cVc/NtPzcPTOtZvFdK77nTTWvCPITSR449+9uB2h7Qqar2v1yd//R0ekhcKwTioHbjO7+D/M6xA/MtPLD8zKFuWQqSVj5v9znetpLMs2wPihT+cIpSf6vTKUNV1fVD3X3K1bY3YsznaD2nKq6bOEyxouenCl4P3PVPi74ZJLM+/3xeYWNazPNFL85yV8kefhC+xuSfF2mEpNkKot4Q3cvXsRjzc8l+U+ZQvavd/ebVujPF1TVt6zY9ztk4wt2fF6Sz6mqK5I8IFN99FOSPH++6EmyvCzkgZnqvE/KVDcOcMSEYuA2Y16J4vhMQfZzMs3qvifTRSgemeRbq+qdmWqNf2v9RTnmfTw408Upzk3yK0mePF+OOclUtlFVZ2daou23qurPMpUzvKq7/9eyfs2rR3xfpjWFfyzT8mqLr/mNSe6X5NwlNcnLvG+tP7NXzPvZlWkd5vMzhd5r5vfwn5O8saoe3t3XzxcCuWumK/g9L8nrl6xTfLtMs9dPz/RZfjTJ+VV18/x+3z7XIC+zb76t6gPrN8wn9X1pprG8d6Za719aV1pyY5Ivq6pvn/uXTPXJayuO/Poh9AHggIRiYMeaL3P8lCT3SnJGppPWTsp0ZbQ3ZLpoxivn0oITMgXjb0/y45muGPfmJF/b3f84r+rw6CSfm+nEufuvW+bsuMx/J86XD/7+qvqFTOUKz0zy01X12iSPnK/Olqr6jkwn1K3VKf9Vkg/OK04srj7xhCT/f5J7VNUT5tc5LsnvdfefrOtDkvyb7A+Sv5bk+PlCHs/OtHzc47v7svnxH5prqp+Qqfzge+f38Ol5tYlk4US3eT+PzXTlvLtnWv/5sZlW4nhmplKQ707y8ap6XZLzF8LxWt3yf8u0QsYqrsj+i4F8RnffMM/6n5bkRzcoVXnu/P5ftOSxl2TJZbcBDpdQDOxY81Xr7pDpAhvvzlQi8fZMJ7D947q2n0jy8iQvn9cjfmKS9y60e1Wm1SheMJ9ktt6J2b/M2do+/yzT7Oldkpw3v+7ihS4en+kEucXLUf/gwuNnZrqQyJqnZQqoa6tQfDDTLPeatdf/y4Wa4pvnfv/HTCfafUt3L666kUxLyt0j0z8gFp208OfausP/kOQbMs0+/0p3v3Oh/eOq6mcyrdZxXj57tnitJvufVr3UclXdkuTOy1aU6O5nHei53f2KqvrtJHfJdMLjmpu7+2hcbQ8YSFn1BmDnm0snPre7r92Efd1uXbg/onYAxwKhGACA4R10sXcAADjWbUtN8V3vetc+/fTTt+OlAQAYyFVXXfWh7t5zsHbbEopPP/30XHnlldvx0gAADKSq/nqVdsonAAAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8FYKxVX1wqq6oqou3ODx3VX1gaq6fL59yeZ2EwAAts5BQ3FVnZtkV3efneSMqjpzSbP7JvnN7t43396z2R0FAICtsspM8b4kl873L0vyoCVtHpDkkVX19nlWeff6BlV1QVVdWVVXXn/99YfdYQAA2GyfFV6XODnJtfP9G5Lcb0mbdyR5WHf/XVX9WpJHJPmdxQbdfUmSS5Jk7969fdg9hsP0gaer6tlp7vWj/lMJgJ1hlVB8U5IT5/unZPns8ru7+5/m+1cmWVZiAQAAO9Iq5RNXZX/JxFlJ3r+kzUuq6qyq2pXkm5JcvTndAwCArbdKKH51kkdX1cVJzktyTVVdtK7N05O8JMm7klzR3a/f3G4CAMDWOWj5RHffWFX7kpyT5NndfV3WzQR3959kWoECAABuc1apKU53fzj7V6AAAIBjiivaAQAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8IRiAACGJxQDADA8oRgAgOEJxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8IRiAACGJxQDADA8oRgAgOEJxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAw1spFFfVC6vqiqq68CDtTquqd25O1wAA4Og4aCiuqnOT7Orus5OcUVVnHqD5zyQ5cbM6BwAAR8MqM8X7klw6378syYOWNaqqr0ny8STXbfD4BVV1ZVVdef311x9GVwEAYGusEopPTnLtfP+GJKetb1BVxyf5r0mestFOuvuS7t7b3Xv37NlzOH0FAIAtsUoovin7SyJO2eA5T0nygu7+yGZ1DAAAjpZVQvFV2V8ycVaS9y9p87Akj6+qy5N8aVX9yqb0DgAAjoLdK7R5dZK3VNU9knxdkkdV1UXd/ZmVKLr7q9buV9Xl3f1dm99VAADYGgcNxd19Y1XtS3JOkmd393VJrj5A+32b1jsAADgKVpkpTnd/OPtXoAAAgGOKK9oBADA8oRgAgOEJxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8IRiAACGJxQDADA8oRgAgOEJxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8IRiAACGJxQDADA8oRgAgOEJxQAADE8oBgBgeEIxAADD27RQXFV3rqpzququm7VPAAA4GlYKxVX1wqq6oqou3ODxU5P8XpKvSPLGqtqziX0EAIAtddBQXFXnJtnV3WcnOaOqzlzS7L5Jntjdz0jyuiT329xuAgDA1lllpnhfkkvn+5cledD6Bt39pu5+W1V9VabZ4ivWt6mqC6rqyqq68vrrrz+CLgMAwOZaJRSfnOTa+f4NSU5b1qiqKsn5ST6c5FPrH+/uS7p7b3fv3bNHdQUAADvHKqH4piQnzvdP2eg5PXl8kncn+cbN6R4AAGy9VULxVdlfMnFWkvevb1BVP1xVj5l/vFOSj2xK7wAA4ChYJRS/Osmjq+riJOcluaaqLlrX5pK5zZuT7MpUewwAALcJuw/WoLtvrKp9Sc5J8uzuvi7J1evafHh+HAAAbnMOGoqTz4TeSw/aEAAAboNc5hkAgOEJxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8IRiAACGJxQDADA8oRgAgOEJxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8IRiAACGt3u7OwCwlR74vAdudxdY4q0/8Nbt7gLArZgpBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhrdSKK6qF1bVFVV14QaP37GqXltVl1XVq6rq+M3tJgAAbJ2DhuKqOjfJru4+O8kZVXXmkmbfluTi7n54kuuSfO3mdhMAALbOKusU70ty6Xz/siQPSvK+xQbd/YKFH/ck+fv1O6mqC5JckCT3ute9DqOrAACwNVYpnzg5ybXz/RuSnLZRw6o6O8mp3f229Y919yXdvbe79+7Zs+ewOgsAAFthlZnim5KcON8/JRsE6aq6c5LnJfnmzekaAAAcHavMFF+VqWQiSc5K8v71DeYT616W5Ee6+683rXcAAHAUrBKKX53k0VV1cZLzklxTVReta/OdSe6X5KlVdXlVnb/J/QQAgC1z0PKJ7r6xqvYlOSfJs7v7uiRXr2vzi0l+cUt6CAAAW2yVmuJ094ezfwUKAAA4priiHQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8IRiAACGJxQDADA8oRgAgOEJxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAw9u93R0AgK3wpq96yHZ3gSUe8uY3bXcXYCkzxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8IRiAACGJxQDADA8oRgAgOEJxQAADE8oBgBgeEIxAADD271Ko6p6YZJ/leT/6+6LNmhzWpKXd/eDN7F/m+7L//OvbXcXWOeqn37MdncBABjcQWeKq+rcJLu6++wkZ1TVmUvanJrkxUlO3vwuAgDA1lqlfGJfkkvn+5cledCSNrckOT/JjRvtpKouqKorq+rK66+//lD7CQAAW2aVUHxykmvn+zckOW19g+6+sbs/eqCddPcl3b23u/fu2bPn0HsKAABbZJVQfFOSE+f7p6z4HAAAuM1YJeBelf0lE2clef+W9QYAALbBKqH41UkeXVUXJzkvyTVVtXQFCgAAuC066JJs3X1jVe1Lck6SZ3f3dUmu3qDtvk3tHQAAHAUrrVPc3R/O/hUoAADgmOKkOQAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMTygGAGB4QjEAAMMTigEAGJ5QDADA8IRiAACGJxQDADA8oRgAgOEJxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIa3e7s7AACwmX7hSb+73V1gne//2W/Y7i4clJliAACGJxQDADA8oRgAgOEJxQAADE8oBgBgeEIxAADDE4oBABieUAwAwPCEYgAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHhCMQAAwxOKAQAYnlAMAMDwhGIAAIYnFAMAMDyhGACA4QnFAAAMb6VQXFUvrKorqurCI2kDAAA70UFDcVWdm2RXd5+d5IyqOvNw2gAAwE5V3X3gBlU/n+T3u/s1VfWoJCd2968eRpsLklww//hFSf58s97EoO6a5EPb3Qm2hbEfl7Efl7Efl7E/cl/Q3XsO1mj3Cjs6Ocm18/0bktzvcNp09yVJLlnh9VhBVV3Z3Xu3ux8cfcZ+XMZ+XMZ+XMb+6FmlpvimJCfO90/Z4DmrtAEAgB1plfB6VZIHzffPSvL+w2wDAAA70irlE69O8paqukeSr0vyqKq6qLsvPECbB2x+V1lHKcq4jP24jP24jP24jP1RctAT7ZKkqk5Nck6SN3f3dYfbBgAAdqKVQjEAABzLnBB3lFXV6dvdh0VV9bSquryqXrrdfbmt22ljeySq6nHzcfH67e7LTnYsjfkyjoNbO9bHe5mRf0eMON7LjHQMCMVHUVX9cJYvabdtuvvHuntfkk9vd19uy3bi2B6J7v7F+bj4m+3uy051rI35Mo6D/UYY72VG/R0x6ngvM9IxIBQfJfO/OD+/u185/3zYl8WuqtOq6i0LPx9XVb9bVW+tqu84lG0cuSVje9THp6ruWVV/M/9r/vKq2jNv/6zjbNVtbGxxzKtqd1V9YOGz/5K5zdOq6h1V9fyF56207RD6cVJVvWvdtsMec8fBclv5HT/C7/295mPuDVV1SU22pS/HknXf7ztW1Wur6rKqelVVHT+32Zbv2VYfe6MfA0Lx0fPoJM9Pjuyy2DWd0PjiTBdMWfMDSa7q7gcm+ZaqusMhbOPILY7tdo3PVyZ5Rnfvm2/XLzvOVt12JB/GID4z5knum+Q3Fz7791TVl2dapvIrkvx9VT1s1W2rdqCqdiW5NMmdFrYd9pg7Dg5oK7/jR/K9/54kj+vur0ny+Um+ZBv7cixZ/H5/W5KLu/vhSa5L8rXb9T07Ssfe0MeAUHz0/Ivufu98f1+mX2ZJcln2r/G8iluSnJ/kxoVti/t7c5K9h7CNI7c4tts1Pg9I8l1V9cdV9cwlr7t2nK26jQNbHPMHJHlkVb19ngHaneQhSV7R05nMr0vy4EPYdiguyK3Xhd+Xwx/zZduYbOV3fNm2lXT3Uxf6dZdMlwLelr4cYz4z3t39gu7+g3n7niR/n+37nh2NY2/ZtmEIxdtj/WWxT1v1id19Y3d/dIX9rbqNTbSN4/PaTH+Z3T/J2VV136P0uiTvSPKw7v6KJMcleUSOwmff3bd099+u23wkr+s4WMEWfMeP+HOvqvOTXDMfD46BLVBVZyc5tbvflm36jI/SsTf0MSAUHz3/WFWnzPc3+7LYy/a36jaO3OLYLnM0xue/d/fHuvuWJO9McuZRet1RLY75u7v77+b7V2Z7P/sjeV3Hwca28jt+RJ97VZ2R5MlJfnC7+3IMudV4V9WdkzwvyVqN7U76jDe7L0MfA0O92W32miTfPN/f7MtiL9vfqts4cotju8zRGJ/XVdXdq+qkJA9P8idH6XVHtTjmL6mqs+Ya329KcnW277M/ktd1HGxsK7/jh/25zzWmv5nkOxZmEB0DR+4z4z2fWPeyJD/S3X89P76TPuPN7svYx0B3ux2FW5LKVKdztySfk+kX58VJ3pvkjknumeQ5h7C/yxfuf0GSa5I8N9N/5e5addvCPl603Z/RbfW2OLabPT5JvjzJU1bow1cn+bMk707y/fO2ZcfZStscF6uPeZL7zJ/7ezKd7JhMEw5vncfyz5Pc+xC27Ury3w6hL4vH2mGPueNgtfHe4HM/ku/4kXzvn5Xk75JcPt8estl9GfEYWPf9flySDy98xudv9vcs2/T73zGw5LPd7g6MdEvyeUn+/Xz/1CTnJfnc+eddSZ5wBPu+x7y/Ox7KtiRPm7/oL93uz+e2fFtSIVUYAAAApUlEQVQc200enzsneewR9OtWx9mq2+ZfBJcnef12f7Y79bbCmJ+Y5FuSnHEY2550tMfccXDE431Y3/Fl2zbhe7+ZfRnyd8QK472Z37Nt+f3vGPjsm8s87xDzf70e192f2O6+sHNU1e2T3NLdx/yi6exXVad0903b3Q+2h+/9WPz+3zmEYgAAhudEOwAAhicUAwAwPKEYAIDhCcUAAAxPKAYAYHj/G3DNY0xv6fazAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df['default_amount']\n",
    "# help(pd.cut)\n",
    "# df['amount_segment'] = pd.cut(df['default_amount'], bins=np.linspace(0, 100000, 101))\n",
    "df['amount_segment'] = pd.cut(df['default_amount'], bins=[0, 1000, 5000, 10000, 20000, 100000])\n",
    "df['repayment_segment'] = pd.cut(df['repayment'], bins=[0, 1000, 5000, 10000, 20000, 100000])\n",
    "# help(np.linspace(0, 10))\n",
    "# np.linspace(0,100000, 101)\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "plt.title('委案金额段占比', size=20)\n",
    "g = sns.barplot(df['amount_segment'].value_counts().index, df['amount_segment'].value_counts().values/len(df))\n",
    "\n",
    "# for index, row in df['amount_segment'].value_counts().values/len(df).iterrows()\n",
    "\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=20)\n",
    "# df['amount_segment'].value_counts()\n",
    "# df['amount_segment'].value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function barplot in module seaborn.categorical:\n",
      "\n",
      "barplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=<function mean at 0x00000163B386EAE8>, ci=95, n_boot=1000, units=None, orient=None, color=None, palette=None, saturation=0.75, errcolor='.26', errwidth=None, capsize=None, dodge=True, ax=None, **kwargs)\n",
      "    Show point estimates and confidence intervals as rectangular bars.\n",
      "    \n",
      "    A bar plot represents an estimate of central tendency for a numeric\n",
      "    variable with the height of each rectangle and provides some indication of\n",
      "    the uncertainty around that estimate using error bars. Bar plots include 0\n",
      "    in the quantitative axis range, and they are a good choice when 0 is a\n",
      "    meaningful value for the quantitative variable, and you want to make\n",
      "    comparisons against it.\n",
      "    \n",
      "    For datasets where 0 is not a meaningful value, a point plot will allow you\n",
      "    to focus on differences between levels of one or more categorical\n",
      "    variables.\n",
      "    \n",
      "    It is also important to keep in mind that a bar plot shows only the mean\n",
      "    (or other estimator) value, but in many cases it may be more informative to\n",
      "    show the distribution of values at each level of the categorical variables.\n",
      "    In that case, other approaches such as a box or violin plot may be more\n",
      "    appropriate.\n",
      "    \n",
      "    \n",
      "    Input data can be passed in a variety of formats, including:\n",
      "    \n",
      "    - Vectors of data represented as lists, numpy arrays, or pandas Series\n",
      "      objects passed directly to the ``x``, ``y``, and/or ``hue`` parameters.\n",
      "    - A \"long-form\" DataFrame, in which case the ``x``, ``y``, and ``hue``\n",
      "      variables will determine how the data are plotted.\n",
      "    - A \"wide-form\" DataFrame, such that each numeric column will be plotted.\n",
      "    - An array or list of vectors.\n",
      "    \n",
      "    In most cases, it is possible to use numpy or Python objects, but pandas\n",
      "    objects are preferable because the associated names will be used to\n",
      "    annotate the axes. Additionally, you can use Categorical types for the\n",
      "    grouping variables to control the order of plot elements.    \n",
      "    \n",
      "    This function always treats one of the variables as categorical and\n",
      "    draws data at ordinal positions (0, 1, ... n) on the relevant axis, even\n",
      "    when the data has a numeric or date type.\n",
      "    \n",
      "    See the :ref:`tutorial <categorical_tutorial>` for more information.    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y, hue : names of variables in ``data`` or vector data, optional\n",
      "        Inputs for plotting long-form data. See examples for interpretation.        \n",
      "    data : DataFrame, array, or list of arrays, optional\n",
      "        Dataset for plotting. If ``x`` and ``y`` are absent, this is\n",
      "        interpreted as wide-form. Otherwise it is expected to be long-form.    \n",
      "    order, hue_order : lists of strings, optional\n",
      "        Order to plot the categorical levels in, otherwise the levels are\n",
      "        inferred from the data objects.        \n",
      "    estimator : callable that maps vector -> scalar, optional\n",
      "        Statistical function to estimate within each categorical bin.\n",
      "    ci : float or \"sd\" or None, optional\n",
      "        Size of confidence intervals to draw around estimated values.  If\n",
      "        \"sd\", skip bootstrapping and draw the standard deviation of the\n",
      "        observations. If ``None``, no bootstrapping will be performed, and\n",
      "        error bars will not be drawn.\n",
      "    n_boot : int, optional\n",
      "        Number of bootstrap iterations to use when computing confidence\n",
      "        intervals.\n",
      "    units : name of variable in ``data`` or vector data, optional\n",
      "        Identifier of sampling units, which will be used to perform a\n",
      "        multilevel bootstrap and account for repeated measures design.    \n",
      "    orient : \"v\" | \"h\", optional\n",
      "        Orientation of the plot (vertical or horizontal). This is usually\n",
      "        inferred from the dtype of the input variables, but can be used to\n",
      "        specify when the \"categorical\" variable is a numeric or when plotting\n",
      "        wide-form data.    \n",
      "    color : matplotlib color, optional\n",
      "        Color for all of the elements, or seed for a gradient palette.    \n",
      "    palette : palette name, list, or dict, optional\n",
      "        Colors to use for the different levels of the ``hue`` variable. Should\n",
      "        be something that can be interpreted by :func:`color_palette`, or a\n",
      "        dictionary mapping hue levels to matplotlib colors.    \n",
      "    saturation : float, optional\n",
      "        Proportion of the original saturation to draw colors at. Large patches\n",
      "        often look better with slightly desaturated colors, but set this to\n",
      "        ``1`` if you want the plot colors to perfectly match the input color\n",
      "        spec.    \n",
      "    errcolor : matplotlib color\n",
      "        Color for the lines that represent the confidence interval.\n",
      "    errwidth : float, optional\n",
      "        Thickness of error bar lines (and caps).         \n",
      "    capsize : float, optional\n",
      "        Width of the \"caps\" on error bars.\n",
      "    \n",
      "    dodge : bool, optional\n",
      "        When hue nesting is used, whether elements should be shifted along the\n",
      "        categorical axis.    \n",
      "    ax : matplotlib Axes, optional\n",
      "        Axes object to draw the plot onto, otherwise uses the current Axes.    \n",
      "    kwargs : key, value mappings\n",
      "        Other keyword arguments are passed through to ``plt.bar`` at draw\n",
      "        time.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    ax : matplotlib Axes\n",
      "        Returns the Axes object with the plot drawn onto it.    \n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    countplot : Show the counts of observations in each categorical bin.    \n",
      "    pointplot : Show point estimates and confidence intervals using scatterplot\n",
      "                glyphs.    \n",
      "    catplot : Combine a categorical plot with a class:`FacetGrid`.    \n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    \n",
      "    Draw a set of vertical bar plots grouped by a categorical variable:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> import seaborn as sns\n",
      "        >>> sns.set(style=\"whitegrid\")\n",
      "        >>> tips = sns.load_dataset(\"tips\")\n",
      "        >>> ax = sns.barplot(x=\"day\", y=\"total_bill\", data=tips)\n",
      "    \n",
      "    Draw a set of vertical bars with nested grouping by a two variables:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.barplot(x=\"day\", y=\"total_bill\", hue=\"sex\", data=tips)\n",
      "    \n",
      "    Draw a set of horizontal bars:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.barplot(x=\"tip\", y=\"day\", data=tips)\n",
      "    \n",
      "    Control bar order by passing an explicit order:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.barplot(x=\"time\", y=\"tip\", data=tips,\n",
      "        ...                  order=[\"Dinner\", \"Lunch\"])\n",
      "    \n",
      "    Use median as the estimate of central tendency:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> from numpy import median\n",
      "        >>> ax = sns.barplot(x=\"day\", y=\"tip\", data=tips, estimator=median)\n",
      "    \n",
      "    Show the standard error of the mean with the error bars:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.barplot(x=\"day\", y=\"tip\", data=tips, ci=68)\n",
      "    \n",
      "    Show standard deviation of observations instead of a confidence interval:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.barplot(x=\"day\", y=\"tip\", data=tips, ci=\"sd\")\n",
      "    \n",
      "    Add \"caps\" to the error bars:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.barplot(x=\"day\", y=\"tip\", data=tips, capsize=.2)\n",
      "    \n",
      "    Use a different color palette for the bars:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.barplot(\"size\", y=\"total_bill\", data=tips,\n",
      "        ...                  palette=\"Blues_d\")\n",
      "    \n",
      "    Use ``hue`` without changing bar position or width:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> tips[\"weekend\"] = tips[\"day\"].isin([\"Sat\", \"Sun\"])\n",
      "        >>> ax = sns.barplot(x=\"day\", y=\"total_bill\", hue=\"weekend\",\n",
      "        ...                  data=tips, dodge=False)\n",
      "    \n",
      "    Plot all bars in a single color:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.barplot(\"size\", y=\"total_bill\", data=tips,\n",
      "        ...                  color=\"salmon\", saturation=.5)\n",
      "    \n",
      "    Use ``plt.bar`` keyword arguments to further change the aesthetic:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.barplot(\"day\", \"total_bill\", data=tips,\n",
      "        ...                  linewidth=2.5, facecolor=(1, 1, 1, 0),\n",
      "        ...                  errcolor=\".2\", edgecolor=\".2\")\n",
      "    \n",
      "    Use :func:`catplot` to combine a :func:`barplot` and a :class:`FacetGrid`.\n",
      "    This allows grouping within additional categorical variables. Using\n",
      "    :func:`catplot` is safer than using :class:`FacetGrid` directly, as it\n",
      "    ensures synchronization of variable order across facets:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> g = sns.catplot(x=\"sex\", y=\"total_bill\",\n",
      "        ...                 hue=\"smoker\", col=\"time\",\n",
      "        ...                 data=tips, kind=\"bar\",\n",
      "        ...                 height=4, aspect=.7);\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(sns.barplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86       3598.00\n",
       "215     10900.00\n",
       "337        50.00\n",
       "356      1622.25\n",
       "527       300.00\n",
       "569      3500.00\n",
       "683      8245.00\n",
       "1166      500.00\n",
       "1230       11.00\n",
       "1563       10.00\n",
       "1567     1699.51\n",
       "1751       10.00\n",
       "1809       20.00\n",
       "2168      500.00\n",
       "2620     1000.00\n",
       "2690     6100.00\n",
       "2842       10.00\n",
       "3243     1785.53\n",
       "3452     1501.76\n",
       "3650     2192.74\n",
       "3716     1500.00\n",
       "3862     3041.12\n",
       "4034      100.00\n",
       "4364      800.00\n",
       "4564       10.00\n",
       "4692      500.00\n",
       "4694      500.00\n",
       "4701      500.00\n",
       "5317      528.00\n",
       "5424     1500.00\n",
       "5439      180.00\n",
       "5515      800.00\n",
       "5541       31.42\n",
       "Name: repayment, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['repayment'] !=0]['repayment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function xticks in module matplotlib.pyplot:\n",
      "\n",
      "xticks(*args, **kwargs)\n",
      "    Get or set the current tick locations and labels of the x-axis.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        locs, labels = xticks()           # Get locations and labels\n",
      "    \n",
      "        xticks(locs, [labels], **kwargs)  # Set locations and labels\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    locs : array_like\n",
      "        A list of positions at which ticks should be placed. You can pass an\n",
      "        empty list to disable xticks.\n",
      "    \n",
      "    labels : array_like, optional\n",
      "        A list of explicit labels to place at the given *locs*.\n",
      "    \n",
      "    **kwargs\n",
      "        :class:`.Text` properties can be used to control the appearance of\n",
      "        the labels.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    locs\n",
      "        An array of label locations.\n",
      "    labels\n",
      "        A list of `.Text` objects.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    Calling this function with no arguments (e.g. ``xticks()``) is the pyplot\n",
      "    equivalent of calling `~.Axes.get_xticks` and `~.Axes.get_xticklabels` on\n",
      "    the current axes.\n",
      "    Calling this function with arguments is the pyplot equivalent of calling\n",
      "    `~.Axes.set_xticks` and `~.Axes.set_xticklabels` on the current axes.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    Get the current locations and labels:\n",
      "    \n",
      "        >>> locs, labels = xticks()\n",
      "    \n",
      "    Set label locations:\n",
      "    \n",
      "        >>> xticks(np.arange(0, 1, step=0.2))\n",
      "    \n",
      "    Set text labels:\n",
      "    \n",
      "        >>> xticks(np.arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue'))\n",
      "    \n",
      "    Set text labels and properties:\n",
      "    \n",
      "        >>> xticks(np.arange(12), calendar.month_name[1:13], rotation=20)\n",
      "    \n",
      "    Disable xticks:\n",
      "    \n",
      "        >>> xticks([])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.xticks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B1</th>\n",
       "      <td>91</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B2</th>\n",
       "      <td>214</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B2+</th>\n",
       "      <td>311</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B3</th>\n",
       "      <td>398</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          min   max\n",
       "category           \n",
       "B1         91   120\n",
       "B2        214   295\n",
       "B2+       311   387\n",
       "B3        398  1956"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cate_default = df[['default_days', 'category']].groupby('category')\n",
    "group_cate = cate_default.min()\n",
    "group_cate['max'] = cate_default.max()['default_days']\n",
    "group_cate.columns = ['min', 'max']\n",
    "group_cate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(18, 6))\n",
    "sns.kdeplot(df['default_days'])\n",
    "# sns.kdeplot(df[df['category']==\"B2\"]['default_days'])\n",
    "# 设置标题\n",
    "plt.title('逾期天数分布图', size=30)\n",
    "\n",
    "# 设置坐标刻度值的大小以及刻度值的字体\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "# 设置横纵坐标的名称以及对应字体格式\n",
    "font2 = {\n",
    "    'weight': 'normal',\n",
    "    'size': 25,\n",
    "}\n",
    "plt.xlabel('天数', font2)\n",
    "plt.ylabel('', font2)\n",
    "for i in group_cate['max']:\n",
    "    plt.vlines(i, 0, 0.0025, colors='g', linestyle='dashed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function xticks in module matplotlib.pyplot:\n",
      "\n",
      "xticks(*args, **kwargs)\n",
      "    Get or set the current tick locations and labels of the x-axis.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        locs, labels = xticks()           # Get locations and labels\n",
      "    \n",
      "        xticks(locs, [labels], **kwargs)  # Set locations and labels\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    locs : array_like\n",
      "        A list of positions at which ticks should be placed. You can pass an\n",
      "        empty list to disable xticks.\n",
      "    \n",
      "    labels : array_like, optional\n",
      "        A list of explicit labels to place at the given *locs*.\n",
      "    \n",
      "    **kwargs\n",
      "        :class:`.Text` properties can be used to control the appearance of\n",
      "        the labels.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    locs\n",
      "        An array of label locations.\n",
      "    labels\n",
      "        A list of `.Text` objects.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    Calling this function with no arguments (e.g. ``xticks()``) is the pyplot\n",
      "    equivalent of calling `~.Axes.get_xticks` and `~.Axes.get_xticklabels` on\n",
      "    the current axes.\n",
      "    Calling this function with arguments is the pyplot equivalent of calling\n",
      "    `~.Axes.set_xticks` and `~.Axes.set_xticklabels` on the current axes.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    Get the current locations and labels:\n",
      "    \n",
      "        >>> locs, labels = xticks()\n",
      "    \n",
      "    Set label locations:\n",
      "    \n",
      "        >>> xticks(np.arange(0, 1, step=0.2))\n",
      "    \n",
      "    Set text labels:\n",
      "    \n",
      "        >>> xticks(np.arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue'))\n",
      "    \n",
      "    Set text labels and properties:\n",
      "    \n",
      "        >>> xticks(np.arange(12), calendar.month_name[1:13], rotation=20)\n",
      "    \n",
      "    Disable xticks:\n",
      "    \n",
      "        >>> xticks([])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.xticks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x25dae9d6400>,\n",
       "  <matplotlib.axis.XTick at 0x25dae9cacf8>,\n",
       "  <matplotlib.axis.XTick at 0x25dae9f10f0>,\n",
       "  <matplotlib.axis.XTick at 0x25dae9f14a8>,\n",
       "  <matplotlib.axis.XTick at 0x25dae9f1ba8>,\n",
       "  <matplotlib.axis.XTick at 0x25dae9f90b8>,\n",
       "  <matplotlib.axis.XTick at 0x25dae9caa58>],\n",
       " <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7sAAAHsCAYAAAATjrqQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xucnndd5//XZ86nzGSSTJOQNE1bDkUOpSWUg6wK2BWtixysInLwJ9rWrQd+rgV/urr70NVdeVRYV92VQqvQB3VZsC3C8qvW9ScFgbZpA0VACy1pkjZJkyaZU+a+5z58f39c96TDkLSTZGau67rn9Xw8eDDXYa77c839R/vu53uIlBKSJEmSJLWTjrwLkCRJkiRpqRl2JUmSJEltx7ArSZIkSWo7hl1JkiRJUtsx7EqSJEmS2o5hV5KkFRARG/KuQZKk1cSwK0la1SKiIyJ6ImI4IrZExAsi4oci4t9GxLuf4vc+HBEvXORndAP/X0T85tPcNxwRvx4R0TruiYiOede75h/PO39R6/8viIhXL6Kejoj4bESct5j6JUkqo668C5AkKS8RsRH4W6ABVIB+YA1wB/A4MBURYymlQxHxRuCX5/36ucD3R8Tueec+mFL66Ek+6k+BTwGvjohvpJRuXVDHi4A/aR2+HHhDRNSAe4CXRcSzgceAGeDfAvfN+90rgD+IiBcACbghIl6YUjr+FK/+w8BASumRp/jb3E/27wmzT/Gcc1NKG5/iuiRJuYmUUt41SJKUi4j4O2AAaLZOrQU2AN+ad1stpfSqiHgr0JFS+khE/DRZWPzgvGf9GLAupfTn8851AO8HBlNKPxcRw8BfA/8A/H5KabZ13yuBnwP+Bng4pXR3RPw58PMppXpE3AtcnlI6tqD+DuBLwO+llD7ZOvffgL6U0lXz7ns58JfAFFAHng3sBybnP44s3F6bUvqHiLgb+MmU0u6IeAXwW8CPpNa/OEREF7A7pbR1sX9vSZJWkmFXkrRqRUQfWeeyjywMdpMF3wuBtwM7ge6U0mxE/EfgFWSB8IXA18g6rZ1kHeEa8LmU0m+3nr0d+CAwAfwSTwbqXuADwPOAm1o/HwHOBz4P/OuU0n2tZ/wG8KtkIfwY8KWU0o/Oq//dZCH48nnnhsgC8N8Av5YW/IM+In4K+NmU0uUR8SHgl0/WBY6IzwNvBY4CX2i9K8BFwHtadX8rpbT9Kf/IkiTlxLArSVq1WmH3TcD/A/wZ8G3gx4H/CnwSuBH47ymlJ1r3byDryt7Ek0Od/xS4MaX0t/OeezHwGeA3W8/vBJ4FTAP7yALvr5INi/5D4CGy4dQbgCeAS4A3AN8LbAW+SNaBflNK6TWtz3gNcDPwvSmlby94r3OAO8mC9s+llP6ldX478HdkAfnbEfEQ8PyU0gwLtMLuO4A/J+v6fo4sRP8c8HqydT8Mu5KkwnLOriRpVYqIAeBu4AHgJ8k6mK8EPp5S+kpEvBT4XeDh1qJPXyMLfW8nC6J3AlWycPoDEdEAPp9SqrZ+/5mtEPkXrc/7T2Th8C/mlfHzrWvfB3yldW5D676/bw0f/hzwE61a3ti6fw1wA9nQ4i9GRH+rtonWM9YB7wJeQjYfmYjYDPxv4DzgttYaWFuBL0VEArYBb0sp/e959TXIutKHybrO7wKuSCk1T7ZQliRJRWLYlSStVrPAy1NKUwAR8TvABSmltwKklA4CV0XEr5HNdb0I+F/AbuBhsk7w35EtIvUa4CrgYuB9rd+fiYj7yEJoE3gGUI2IX6Q19DmldFHr3rsiYpYsvO4CXh4RHyCbVwuwfd7PpJQmI+K5rTm/N0bEe4FHU0p/1HqXO4B/Tind0Dq+GLgV+CPg3SmlF7XOfwt4aUqpEhF/wXcvRnUu8B9bP98C/CNwdUTcAPziaf21JUlaYYZdSdJqdQXwnlbI7AVeBNwdEf9ANn+2xpNDjq8FfoFsReQusuD7w8DPkA1BbrbO/xCtsNvyWrI5uB+d39mNiH9HNkwagIh4PVl4PgIMtk7fRbaQVLT+N8W8LQPnFrdq+X7gV+Ydb2nVPmcc+I2U0seeajslstWc59tL1sl+DHgb8PtkHeWryVavliSpsAy7kqRVqbV68dwKxv+VLNz+B7Jg91Hg37S6u3Pmhhw/A/gI8GWyObhbgF9MKc1fwXlOF/CHEXHX3ImIGASuI+sSz9Vye2sv3ueklP5Tq8u6GzhANhT5H4FXkS149R1aq0B3ppS+NO/0ZuDRec/f3XoezAvM857RA6wnW6n5xOnW7z4aEe8hm3v8V8BnU0rjrdWY4yTvLElSITjfRpIk+HfAr5PNi/0aWTf3tRGxbu6GiHhmqzv7YbK5q58hW635WuCDEfGRiPiR1mrIAKSU9pOtWvwz8z7rZ4GPpJT2LqghAT/fWhjqh1vHj5EtBvVfgGuAfzV/rmwr6P53sq4zETHcmudbSSlVT/GuvfN+7iJbgfpfyObsPjDvWve89/gD4EfJusvPWfD7kiQVkv+QkiStWhHxC2RDgJ9FthjTZ8gWgRoDfhq4PyL+nqzj+37ggymlf9/63VcCPSmlB4FXRcQPAm8h6/hOtebcvrL1UTWy7YNqwBBwNCJ+mGyF5WenlBpAT+v5c53d1wOXAz/V2uv2x4D/CYwCT0TEHwE/CLxubqsiskD8duA3nuK1nzHv526yfxd4dkqptuC+LuAzrWHeC/9uvz3vHkmSCsmthyRJq1ZEPA84B9iVUjp2kuvdwPaU0jdzqC2AvpNtC9S6voasg7swpC7V568HjrWCuCRJpWPYlSRJkiS1HefsSpIkSZLajmFXkiRJktR2SrWwxIYNG9L27dvzLkOSJEmStAzuu+++wymlsaV4VqnC7vbt29m5c2feZUiSJEmSlkFEPLJUz3IYsyRJkiSp7Rh2JUmSJElt52nDbkRsjIg7IuLjEbF2wbX+iLg9Im6LiPMjc3Pr3GWte94XEbdGxOtax0MR8dkFz9kcEX+9lC8mSZIkSVq9FtPZvQ54N/Be4G0Lrr0TuAm4BrgWuAK4F7gSuCYing+QUnpj6xzArwCx4Dm/BbhpvSRJkiRpSSwm7G4Fvgo8AJy/4Np2YFdK6SAwMO+4BlTmjlv3TgKklH4PODL3gIh4JfAgMH6yD4+IqyJiZ0TsPHTo0KJeSpIkSZK0ui0m7DbIOrGdwNCCa1Wgu/XzyCKOv0NEdAJXAX9yqg9PKd2QUtqRUtoxNrYkK1BLkiRJktrcYsLuTuBS4BJg78muRcQWYGbecT/QT9bVvTgiAlh3kmc/D+gDrgcui4jXntFbSJIkSZI0z2LC7k3Au8jm7d4VEW+ed+3TwOXAB4DrU0q7gDHgE8D7U0qHyQLyp4AbFz44pfRASuknUkrvAu5JKd1xVm8jSZIkSRIQKaW8a1i0HTt2pJ07d+ZdhiRJkiRpGUTEfSmlHUvxLPfZlSRJkiS1HcOuJEmSJKntGHYlSZIkSW3HsCtJkiRJajuGXUmSJElS2zHsSpIkSZLajmFXkiRJktR2DLuSJEmSpLZj2JUkSZIktR3DriRJkiSp7XTlXYCWzi137zmj33vLS7ctcSWSJEmSlC87u5IkSZKktmPYlSRJkiS1HcOuJEmSJKntGHYlSZIkSW3HsCtJkiRJajuGXUmSJElS2zHsSpIkSZLajmFXkiRJktR2DLuSJEmSpLZj2JUkSZIktR3DriRJkiSp7Rh2JUmSJEltx7ArSZIkSWo7hl1JkiRJUtsx7EqSJEmS2o5hV5IkSZLUdgy7kiRJkqS2Y9iVJEmSJLUdw64kSZIkqe0YdiVJkiRJbcewK0mSJElqO4ZdSZIkSVLbMexKkiRJktqOYVeSJEmS1HYMu5IkSZKktmPYlSRJkiS1HcOuJEmSJKntGHYlSZIkSW3HsNumGs3E33ztAIcnq3mXIkmSJEkrzrDbpvYdPc5nHzzEBz/3MIcMvJIkSZJWGcNum9o/XgGg3kx8yMArSZIkaZUx7Lap/eMz9Hd3ctX3XUATDLySJEmSVhXDbpvaP15h80gfG4f7+LlXnm/glSRJkrSqGHbbUDMlDk5kYRcw8EqSJEladQy7bejwVJVaI7F5pP/EufmB98bPP8xsvZlfgZIkSZK0zAy7bWhucarNa/u+4/zG4T7e8KItTFTq7Dt6PI/SJEmSJGlFGHbb0IHxCp0RjK3p/a5r29YPALDv6MxKlyVJkiRJK8aw24b2j89wznAvXR3f/fUO9XaxdqCbfccMu5IkSZLal2G3De0fr7BpuO+U17eu7edRhzFLkiRJamOG3TYzVa0zWamzeW3/Ke/ZOjrA0eM1pqr1FaxMkiRJklaOYbfN7B/PhifPbTt0MltGsyD8qPN2JUmSJLUpw26b2X+stRLzUwxj3rK2nwD2HXMosyRJkqT2ZNhtM/vHZxjp72agt+uU9/R1d7JhqNfOriRJkqS29bRhNyI2RsQdEfHxiFi74Fp/RNweEbdFxPmRubl17rLWPe+LiFsj4nWt46GI+Oy8Z/xORPx9RNy01C+3Gu0frzzlEOY5W0f72Xd0hpTSClQlSZIkSStrMZ3d64B3A+8F3rbg2juBm4BrgGuBK4B7gSuBayLi+QAppTe2zgH8ChAAEdEN7E4pvRoYjYinT2k6pVqjyeGp6qLC7pbRfqaqdcZnaitQmSRJkiStrMWE3a3AV4EHgPMXXNsO7EopHQQG5h3XgMrcceveSYCU0u8BR1o/11JKN0VEF9CXUqqczcusdo9PVGkm2DRy6pWY52wdHQBgn0OZJUmSJLWhxYTdBlknthMYWnCtCnS3fh5ZxPGp/Drwxye7EBFXRcTOiNh56NChRZS7es2txPyMRXR2N4/00RHw6DHDriRJkqT2s5iwuxO4FLgE2HuyaxGxBZiZd9wP9JN1dS+OiADWnezhEfH9wLqU0mdOdj2ldENKaUdKacfY2Nhi3mnV2j9eoaerg9HBnqe9t7uzg00jfew76orMkiRJktrPYsLuTcC7yObt3hURb5537dPA5cAHgOtTSruAMeATwPtTSofJAvKngBtP8fybgQ0R8aGI2HRmryHIOrubhvvoiFjU/VvXDvDosRmaTRepkiRJktReTr0/TUtKaRx46ymu1YCrF5x7z4Lj64HrF5x7/byft51GvTqFlBL7xyu86Ny1T39zy9bRfu7ZfYTdT0xzwdjCEeqSJEmSVF7us9sm9h2doVpvsnkRi1PN2TKa3fvAvvHlKkuSJEmScmHYbRNfe2wCYFHbDs05Z00f3Z3BV/YdW66yJEmSJCkXht028Y39EwSwcXjxYbezI3jGSL+dXUmSJEltx7DbJr6+f4INQ730dJ3eV7p1tJ+vPTZOvdFcpsokSZIkaeUZdtvEN/ZPsOk0hjDP2TI6QKXW5MGDU8tQlSRJkiTlw7DbBsZnauw7OsMzziDsbj2xSJXzdiVJkiS1D8NuG/jn/dniVJtOYyXmOesHexju6+IrztuVJEmS1EYMu23g662wu3nt6Xd2I4IXbl1rZ1eSJElSWzHstoFv7J9gw1APa3q7zuj3X7h1hH85MEml1ljiyiRJkiQpH4bdNvD1/RM8d/MwEXFGv//CrWupN9OJDrEkSZIklZ1htw3sPTLD9vWDZ/z7F587AsADex3KLEmSJKk9GHZLrtZoMj5TY/1Qzxk/Y9NwH2NrennARaokSZIktQnDbskdO14DYN3gmYfdiODirSN89VHDriRJkqT2YNgtuSPTs8DZhV2AZ21cw+4npqk1mktRliRJkiTlyrBbck9MV4GzD7sXjg1RayT2Hjm+FGVJkiRJUq4MuyU319ldP9h7Vs955jlDADx0aPqsa5IkSZKkvBl2S24u7I4Odp/Vcy4Yy1Zz/tbjU2ddkyRJkiTlzbBbcifC7sDZDWMe7uvmnDW9PHTIsCtJkiSp/Ay7JXdkepaR/m66O8/+q7xwbMiwK0mSJKktGHZL7onpWdaf5eJUcy48Z5CHHp8ipbQkz5MkSZKkvBh2S+7I1OxZr8Q858KxISYqdQ5PzS7J8yRJkiQpL4bdkjt6fJbRJQy7gEOZJUmSJJWeYbfklnYYs2FXkiRJUnsw7JZYSomj00s3jHnzcB/93Z089Lh77UqSJEkqN8NuiU3M1Kk305KF3Y6OyBapsrMrSZIkqeQMuyX2xHQVYMnCLrj9kCRJkqT2YNgtsaPHs1WTlzrsPnpshpnZxpI9U5IkSZJWmmG3xJ5obRG0frB3yZ554dgQKcHDh+3uSpIkSSovw26JHZludXaHlrCze84gAA8dcpEqSZIkSeVl2C2xJ+bC7sDShd3t6weJgIcet7MrSZIkqbwMuyV2dHqW/u5O+ns6l+yZfd2dnDs64CJVkiRJkkrNsFtiR5Zwj935LhwbdBizJEmSpFIz7JbYE9OzrF/C+bpzLhwb4uFDUzSbacmfLUmSJEkrwbBbYsvV2X3mOUNU600ePTaz5M+WJEmSpJVg2C2xI9OzS7o41ZwLzxkCcN6uJEmSpNLqyrsAnbml6uzecvee7ziertYB+PjOfTx2rHLS33nLS7ed9edKkiRJ0nKxs1tSM7MNZmqNJd1jd85gbxcDPZ08Plld8mdLkiRJ0kow7JbUE9NZEF2/DHN2AcaGejlk2JUkSZJUUobdkjoyPQvAusHeZXn+2JpeDk0ZdiVJkiSVk2G3pJ4Mu93L8vyxNb1MV+scn60vy/MlSZIkaTkZdktq2Tu7Q9lzDzuUWZIkSVIJGXZL6smwu0xzdtdkYdehzJIkSZLKyLBbUk9Mz9LdGQz3Lc/uUaODPXR2hItUSZIkSSolw25JHZmaZXSgh4hYlud3RLBhqMewK0mSJKmUDLsldeT47LINYZ4zNuSKzJIkSZLKybBbUkemVyDsrunlyPQs9WZzWT9HkiRJkpaaYbekVirsNhM8MTW7rJ8jSZIkSUtteVY30lm55e49T3vP/vEZNg73LereMzU21AfAockqG4f7lu1zJEmSJGmp2dktoUYzUak1GezpXNbP2bAm6xwfdt6uJEmSpJIx7JbQ8dk6AIO9y9uY7+3qZLivi8MOY5YkSZJUMobdEpquNoDlD7sAawd6OHbcsCtJkiSpXAy7JTQ919ld5mHMAGsHujk2U1v2z5EkSZKkpWTYLaHp6soMYwZY29/D+EyNZkrL/lmSJEmStFQMuyU0PZsNYx5Yoc5uo5mYqtSX/bMkSZIkaak8bdiNiI0RcUdEfDwi1i641h8Rt0fEbRFxfmRubp27rHXP+yLi1oh4Xet4KCI+O+8Zb4mIT0XEby/1y7Wruc7uQM/yd3ZHB7oBnLcrSZIkqVQW09m9Dng38F7gbQuuvRO4CbgGuBa4ArgXuBK4JiKeD5BSemPrHMCvAAEQEQG8I6X0b4ANC8O0Tm66Wqe/u5POjlj2zxoZyLYfOuq8XUmSJEklspiwuxX4KvAAcP6Ca9uBXSmlg8DAvOMaUJk7bt07CZBS+j3gSOvcILCv9fPXgG1n8A6rzvRsY0Xm6wKM9med3fHjhl1JkiRJ5bGYsNsg68R2AkMLrlWB7tbPI4s4XmgWmEttJ3s+EXFVROyMiJ2HDh1aRLnt73i1viIrMQP0dnfS393JUYcxS5IkSSqRxYTdncClwCXA3pNdi4gtwMy8436gn6yre3FruPK6hQ9OKc0CnRHRcYrnk1K6IaW0I6W0Y2xsbPFv1samZ+sr1tmF1vZDdnYlSZIklchiwu5NwLvI5u3eFRFvnnft08DlwAeA61NKu4Ax4BPA+1NKh8kC7KeAG0/x/A8DtwKHUkrfFXb13aarDQZ7V6azC7C2v5tjM3Z2JUmSJJXH07YHU0rjwFtPca0GXL3g3HsWHF8PXL/g3Ovn/XwncOfiS17dmilxfLbO4AqsxDxn7UAPDx+eJqVE1qSXJEmSpGJzn92SqdQaNBMrPoy5Wm9SqTVX7DMlSZIk6WwYdkvmeLUBwMAKLVAFWWcXcCizJEmSpNIw7JbM9GwdWNnO7uhAtqC2i1RJkiRJKgvDbslMV1c+7I609tp1+yFJkiRJZWHYLZnp1jDmldpnF2Cot4uujrCzK0mSJKk0DLslk8cw5ojI9tqdMexKkiRJKgfDbslMV+v0dHbQ3bmyX93agR6OOYxZkiRJUkkYdktmerbBYO/KDWGes7a/22HMkiRJkkrDsFsy09X6ig5hnrN2oJupap1aw712JUmSJBWfYbdkpmfrDPbkEXazvXbH7e5KkiRJKgHDbskcrzYYWMGVmOesndtr10WqJEmSJJWAYbdkpmfzGcY82p91dl2kSpIkSVIZGHZLZLbepNZIuYTd4f5uAjjqMGZJkiRJJWDYLZHpamuP3RyGMXd2BMP93XZ2JUmSJJWCYbdEpmdbYTeHzi60th9yzq4kSZKkEjDslsh0tQHk09mFbJEqO7uSJEmSysCwWyK5d3YHehifqdFMKZfPlyRJkqTFMuyWyIk5u7mF3W6aCSYr9Vw+X5IkSZIWy7BbItPVBp0R9Hbl87WtdfshSZIkSSVh2C2R47N1Bno7iYhcPn/tQDfg9kOSJEmSis+wWyKVWoP+7nwWp4Inw+64nV1JkiRJBWfYLZFqvZnbEGaA3q5O+rs7Oer2Q5IkSZIKzrBbIpVag74cO7sAo24/JEmSJKkEDLslkndnF7Lth445Z1eSJElSwRl2S6Rab9Kbc2d37UA3x47XSO61K0mSJKnADLslUqk1CtHZnW00GXferiRJkqQCM+yWRDMlZutNerty7uz2Zysy7zs6k2sdkiRJkvRUDLslUas3SUBfd96d3SzsPnrMsCtJkiSpuAy7JVGpNwHy7+wO9ADwqJ1dSZIkSQVm2C2Jaq0BQG/Ond3Bnk66O8POriRJkqRCM+yWRLXV2e3LeYGqiGBtf4+dXUmSJEmFZtgtiWpBhjFDNm/3sXHDriRJkqTiMuyWRKUgw5ghC7t2diVJkiQVWf7JSYvy5DDmInR2e3hiepaZ2UbepUiSJEnSSRl2S6JaL1Bnt9/thyRJkiQVW/7JSYtSqRVpzm5r+yHDriRJkqSCMuyWRLXeoKsj6OyIvEs50dk94CJVkiRJkgrKsFsS1VqT3u78u7oAa/q7ANg/Xsm5EkmSJEk6OcNuSVTqjdz32J3T1dHBhqFeDhh2JUmSJBVUMdKTnlbW2S3O17V5pM/OriRJkqTCKk560lOq1huFWJxqzqaRPju7kiRJkgrLsFsS1XqzMMOYYa6z6wJVkiRJkoqpOOlJT6laL84CVZB1dicqdaar9bxLkSRJkqTvYtgtiUqtQW/BOrsAByYcyixJkiSpeIqTnvSUqvUmfUXq7A73AzhvV5IkSVIhGXZLoN5o0mimQnZ2XZFZkiRJUhEVJz3plCr1JkDh5uwCHHCRKkmSJEkFZNgtgWqtAVCozm5fdyejA912diVJkiQVUnHSk06p2ursFmnrIYBNI/3O2ZUkSZJUSMVKTzqpSr3V2S3QMGaY22vXsCtJkiSpeAy7JVCttebsFq6z2+fWQ5IkSZIKqVjpSSdVbXV2+7oK1tkd7uPI9CyV1pxiSZIkSSoKw24JVE+sxlysr2tuReaDdnclSZIkFUyx0pNO6slhzAXr7I70A7hIlSRJkqTCMeyWQKXeoCOguzPyLuU7nNhr186uJEmSpIJ52rAbERsj4o6I+HhErF1wrT8ibo+I2yLi/Mjc3Dp3Weue90XErRHxutbxda3jq1vHvx0RH4mIP1qOF2wH1VqT3q5OIooZdl2RWZIkSVLRLKazex3wbuC9wNsWXHsncBNwDXAtcAVwL3AlcE1EPB8gpfRG4MpWWH5J6/h7W894bkrp7cBIRAyf5fu0pWq9Ubj5ugBDvV2s6etyGLMkSZKkwllMgtoKfBV4ADh/wbXtwK6U0kFgYN5xDajMHbfunZz3LIBHW+H2eET8PpBSShMLPzwiroqInRGx89ChQ6fxau2jUmsWbiXmOdleuzN5lyFJkiRJ32ExYbcBBNAJDC24VgW6Wz+PnObx3PPWArcD1YXDpAFSSjeklHaklHaMjY0t5p3aTrXeKNweu3M2jfTb2ZUkSZJUOItJUDuBS4FLgL0nuxYRW4CZecf9QD9ZV/fiyCabrgMeJuv2AlwE1IFKSukeYDff3TkW2dZDRRzGDNleu87ZlSRJklQ0i0lQNwHvIpu3e1dEvHnetU8DlwMfAK5PKe0CxoBPAO9PKR0mC8ifAm5MKTWAOyPik8CdKaXHgXpEfA54BU8OcdY8ldYCVUW0aaSPQ1NVao1m3qVIkiRJ0gldT3dDSmkceOsprtWAqxece8+C4+uB6+cd3wzcPO/4HadX8upT5GHMm0f6SAken6yyZW1/3uVIkiRJEuA+u6VQrTXp6y5uZxfggItUSZIkSSoQw27BNVNittEscGc36+Y6b1eSJElSkRQzQemE2Xo2F7a38J1dw64kSZKk4jDsFlyl1gCgr6Cd3eG+LgZ6Ou3sSpIkSSqUYiYonVAteGc3Itg00mdnV5IkSVKhGHYLrtrq7BZ1zi7ApuE+9rtAlSRJkqQCKW6CEgCVVme3qMOYATu7kiRJkgqnuAlKQPGHMUO21+7BySqNZsq7FEmSJEkCDLuFV4phzCP9NJqJw1PVvEuRJEmSJMCwW3hzw5h7uwrc2R3Oth9yRWZJkiRJRWHYLbgTnd3u4n5VT+616yJVkiRJkoqhuAlKQDZnt6ezg46IvEs5pc0jdnYlSZIkFYtht+Cq9Uahu7oA6wZ76OnscEVmSZIkSYVR7BQlKrVmoefrAkQEm0b67OxKkiRJKgzDbsFV6w36Ct7ZhdZeuxOGXUmSJEnFUPwUtcpVa81Cbzs0Z/NIn8OYJUmSJBVG8VPUKletF38YM7Q6u+MVUkp5lyJJkiRJht2iq5RkGPPm4T5mG02OTM/mXYokSZIkGXaLrlqCBaoANo30A24/JEmSJKkYDLsFllLKth4qyZxdwHm7kiRJkgqh+ClqFas1Es0Evd3F7+zOhd39rsgsSZIkqQAMuwVWrTcAStHZXT/US1dHcGB8Ju9SJEmSJMmwW2TVWhOgFAtUdXYEG4f7nLMrSZIkqRCKn6JWsWo9C7tlWKAKntx+SJIkSZLyZtgtsMrcMObw7gHYAAAgAElEQVQSdHbBsCtJkiSpOMqRolapE8OYS9LZ3dwaxpxSyrsUSZIkSaucYbfAyrRAFWSd3Zlag2PHa3mXIkmSJGmVK0eKWqUqc3N2S7D1EMC56wYA2Hv0eM6VSJIkSVrtDLsFVq1lnd2+knR2z1ufhd1HnjDsSpIkScpXOVLUKlWtN+nsCLo6y/E1nTuahd09Rwy7kiRJkvJVjhS1SlVqjdLM1wUY7O1iw1Ave+zsSpIkScpZeZLUKlStN0sVdgG2reu3sytJkiQpd+VKUqtMtdagrySLU805b/2gYVeSJElS7gy7BVYpYWf33HUDPDY+w2xrJWlJkiRJykO5ktQqM1tv0ttVss7uugFSgn1uPyRJkiQpR4bdAqvUGvR2l+sr2rbeFZklSZIk5a9cSWqVqdab9JWwswuGXUmSJEn5MuwWWLVevs7u2Jpe+ro73H5IkiRJUq7KlaRWkUYzUWuk0s3ZjQi2rRvgETu7kiRJknJk2C2oar0BQF/JOrsA29YNsNewK0mSJClH5UtSq0S1lm3dU7bOLsC2ddleuymlvEuRJEmStEoZdguq0urslm2fXYBt6/o5Ptvg8NRs3qVIkiRJWqXKl6RWiROd3RIOYz5v/SAAe45M51yJJEmSpNWqfElqlTgxZ7eEw5jPdfshSZIkSTkz7BZUtT43Z7d8X9HW0X4i4BG3H5IkSZKUk/IlqVXiyWHM5evs9nV3smm4z86uJEmSpNwYdguqcmIYczm/om3rBthjZ1eSJElSTrryLkAnV603CaC7oGH3lrv3POX1eiPx4OOT33XfW166bTnLkiRJkiTAzm5hVWsNero66IjIu5Qzsm6oh8lKndnW3GNJkiRJWkmG3YKq1Jv0lXC+7px1Az0AHD3uXruSJEmSVp5ht6CqtUYpV2Kes24wC7tHpg27kiRJklZeedNUm6vWm4ZdSZIkSTpD5U1Tba5Sa5Ry26E5Az2d9HZ18IRhV5IkSVIODLsFVfbObkSwbrCHo4ZdSZIkSTl42jQVERsj4o6I+HhErF1wrT8ibo+I2yLi/Mjc3Dp3Weue90XErRHxutbxda3jq+c95z9HxIuW+uXKrFpv0tdV3s4uZEOZ7exKkiRJysNiWofXAe8G3gu8bcG1dwI3AdcA1wJXAPcCVwLXRMTzAVJKbwSubIXll7SOvxcgIi4ChlNKXz7712kf1XqD3u7ydnYhC7tHj8/STCnvUiRJkiStMotJU1uBrwIPAOcvuLYd2JVSOggMzDuuAZW549a9k/OeBfBoRAwDfwhcGBF/FhHdCz88Iq6KiJ0RsfPQoUOn8WrllVKiWmvS2wad3UYzMTFTy7sUSZIkSavMYsJuAwigExhacK0KzAXUkdM87gRGgaGU0muBB4HLF354SumGlNKOlNKOsbGxxbxT6c02miSgrw06uwBH3GtXkiRJ0gpbTJraCVwKXALsPdm1iNgCzMw77gf6ybq6F0dEAOuAh8m6vQAXAUeBb7WOp4H6Gb9JG6nWmgCl7+yuH+wF4MiUYVeSJEnSylpM2L0JeBfZvN27IuLN8659mqwb+wHg+pTSLmAM+ATw/pTSYbKA/CngxpRSA7gzIj4J3JlSmgAejog/B14F/J8leq9Sq9QbAKWfszvS301H2NmVJEmStPK6nu6GlNI48NZTXKsBVy84954Fx9cD1887vhm4ed7x751eye1vrrPbV+KthwA6O4K1Az0ccUVmSZIkSSus3GmqTVXr7TGMGWCdYVeSJElSDgy7BVSptccwZsgWqTLsSpIkSVpp5U9TbaitOruDPRyfbZwI8JIkSZK0Egy7BTQXDMu+9RDM237I7q4kSZKkFVT+NNWGqnOrMbdJZxcMu5IkSZJWlmG3gCq1Jt2dQWdH5F3KWTPsSpIkScqDYbeAKrUGfd3l7+oC9HV3MtDTadiVJEmStKIMuwVUrTfbYgjzHFdkliRJkrTSDLsFlHV22+erGRvq5cBEhZRS3qVIkiRJWiXaJ1G1kWq92TbDmAHOXTfAVLXO0eO1vEuRJEmStEoYdguoUmvQ29U+X822dQMA7DlyPOdKJEmSJK0W7ZOo2kg7LVAFsHG4j57ODsOuJEmSpBVj2C2gar1JXxt1djs7gq2j/ew5Mp13KZIkSZJWifZJVG2i0UzZasxt1NkF2LZ+gAPjFY7P1vMuRZIkSdIqYNgtmKlqFgbbaRgzZPN2mwke2DeedymSJEmSVgHDbsGcCLttNIwZYNtotkjV/XuO5lyJJEmSpNWgvRJVG5isZNvztNsw5oHeLjYM9XD/I8fyLkWSJEnSKmDYLZipSnt2diEbyrxrz1FSSnmXIkmSJKnNtV+iKrnJSnvO2QU4d90AT0zPugWRJEmSpGVn2C2YiRPDmNvvq9m2znm7kiRJklZG+yWqkntygar26+xuHO5jqLfLebuSJEmSlp1ht2DaeRhzRwQXnzvCfY/Y2ZUkSZK0vAy7BTNVqdMR0N0ZeZeyLC7dNso/H5hgutXBliRJkqTlYNgtmMlKjd6uTiLaN+w2E3xln0OZJUmSJC0fw27BTFbq9LXh4lRzLtm2FoBdewy7kiRJkpZP+6aqkpqs1ultw8Wp5qwd6OHCsUHud96uJEmSpGVk2C2YyUqtrTu7kA1l3rX3GCmlvEuRJEmS1KbaO1WVUDaMuX07uwCXnjfKkelZdj9xPO9SJEmSJLUpw27BTFXr9Ha199dy6bZRAIcyS5IkSVo27Z2qSmg1dHafdc4Qa3q7uH+PYVeSJEnS8jDsFsxUpb0XqALo6AhetG0t99nZlSRJkrRMDLsFUqk1mG00236BKoBLto3y4MFJpqr1vEuRJEmS1IbaP1WVyGQlC37tPowZ4NJta2km+Mpe99uVJEmStPQMuwUy1+Vs9wWqAC45N1ukauduhzJLkiRJWnrtn6pKZLJSA1ZHZ3dkoJsXnzfKpx94zP12JUmSJC05w26BrKZhzAA//uKtfPPxKb6ybzzvUiRJkiS1GcNugcyF3dUwjBngihdupq+7g0/ctzfvUiRJkiS1mdWRqkpiNQ1jBhju6+a1z9vEX3/5MSq1Rt7lSJIkSWojht0CmVugqm+VdHYBfvzF5zJRqXPn1w/mXYokSZKkNrJ6UlUJnBjGvEo6uwAvv3A9zxjp4xP37cu7FEmSJEltxLBbIJOVGv3dnXR2RN6lrJjOjuBNL97K5755iAPjlbzLkSRJktQmDLsFMlWtM9TXlXcZK+5Nl26lmeDWXXZ3JUmSJC2N1ZesCmyiUmdNm4fdW+7ec9Lz29cPcNPndzPS103Ed3e23/LSbctdmiRJkqQ2Yme3QKYqddb0tnfYPZVLt41yeKrK3iPH8y5FkiRJUhsw7BbIZKXGmr7uvMvIxQu2jNDdGdy351jepUiSJElqA4bdAplcBcOYT6W3u5PnP2OEB/Ydo9Zo5l2OJEmSpJIz7BbIVLXO0Codxgxw6XmjVOtNvvbYRN6lSJIkSSo5w26BZJ3d1TmMGeD8DYOMDnRz/56jeZciSZIkqeQMuwXRaCamqqt3GDNARwSXbBvlocenOHZ8Nu9yJEmSJJWYYbcgpmfrAKs67EK2KnMC7nvE7q4kSZKkM2fYLYjJimEXYN1gD886Z4idjxyl0Ux5lyNJkiSppAy7BTHVCrtDvat3zu6cl2xfx/hMjQcPTuZdiiRJkqSSMuwWxGSlBtjZBXju5mHW9HZxz7eP5F2KJEmSpJIy7BaEw5if1NkRvHj7KA8enHShKkmSJEln5GnDbkRsjIg7IuLjEbF2wbX+iLg9Im6LiPMjc3Pr3GWte94XEbdGxOtax9e1jq+e95zNEfHXS/1yZTJZNezO95Lt6wC4d7cLVUmSJEk6fYvp7F4HvBt4L/C2BdfeCdwEXANcC1wB3AtcCVwTEc8HSCm9EbiyFZZf0jr+3nnP+S2gcRbvUXpPDmN2zi7A6EAPz9o4xH2PHHGhKkmSJEmnbTFhdyvwVeAB4PwF17YDu1JKB4GBecc1oDJ33Lp3ct6zAB6NiOGIeCXwIDB+xm/RBiZPLFBlZ3fOZdvXM1Gp8y8HJvIuRZIkSVLJLCbsNoAAOoGhBdeqwFwrcuQ0jzuBNcBVwJ+c6sMj4qqI2BkROw8dOrSIcstpqlKnsyMY6OnMu5TCeM6mNQz3dXHPbheqkiRJknR6FhN2dwKXApcAe092LSK2ADPzjvuBfrKu7sUREcA64GGybi/ARcB6oA+4HrgsIl678MNTSjeklHaklHaMjY2d5uuVx2SlxlBvF9mfSpAtVLVj+zq+eXCKvUeO512OJEmSpBJZTNi9CXgX2bzduyLizfOufRq4HPgAcH1KaRcwBnwCeH9K6TBZQP4UcGNKqQHcGRGfBO5MKT2QUvqJlNK7gHtSSncs2ZuVzGS17hDmk9hx3igAH7t34X9nkSRJkqRTe9p0lVIaB956ims14OoF596z4Ph6ss7t3PHNwM0nedbPLKriNjVZqbsS80msHejhOZvW8LGde/mVH3wW3Z3uliVJkiTp6ZkcCmKyUjPsnsJLtq/j0GSV//ONg3mXIkmSJKkkDLsFMVWtu+3QKTx74xo2j/Tx0bv35F2KJEmSpJIw7BaEw5hPrbMjePNLtvG5bx5m9+HpvMuRJEmSVAKG3YKYrLhA1VP5qcvOpasjuPlLj+RdiiRJkqQSMOwWxFTFYcxP5ZzhPn74BZv5Xzv3cny2nnc5kiRJkgrOsFsAlVqD2UbTYcxP4x0vP4/JSp3bdz2WdymSJEmSCs6wWwBT1axTadh9ai8+b5Tv2TzMR764m5RS3uVIkiRJKjDDbgFMVgy7ixERvOMV5/HPBya559tH8i5HkiRJUoEZdgtgslIDYKjXObtP58detIW1A918+Iu78y5FkiRJUoEZdgtgys7uovV1d/KTO87lb752kP3jM3mXI0mSJKmgDLsFMGHYPS1vfdl5NFPilrv35F2KJEmSpIIy7BbAiQWqHMa8KOeuG+A1F53DX96zh2q9kXc5kiRJkgrIsFsAc3N27ewu3ttfvp3DU7P8v189kHcpkiRJkgrIsFsAc6sxDxl2F+2Vz9zABWOD/MUXduddiiRJkqQCMuwWwFS1Tl93B92dfh2L1dERvP1l5/Hlvcf4yt5jeZcjSZIkqWBMVwUwWamxps/5uqfrTS/eymBPp91dSZIkSd/FsFsAE5U6a3odwny61vR189MvO4/bv/wou/YczbscSZIkSQVi2C2AqUrdxanO0C+/5lmcs6aX37ztn6g3mnmXI0mSJKkgDLsF4DDmMzfU28Vv/+jz+Pr+CW7+0iN5lyNJkiSpIAy7BTBVrTPkMOYz9iMv2MT3PXuMP/zbBzk4Ucm7HEmSJEkFYNgtgEmHMZ+ViOB3Xvc8ZhtNfvfTX8+7HEmSJEkFYNgtgMlK3T12z9L2DYNc+wPP5NMP7Odz3zyUdzmSJEmScmbYzVmzmZiq1p2zuwSu/v4LOH/DIL91+z9RqTXyLkeSJElSjgy7OZuarQMwbGf3rPV1d/I7P/Y8dj9xnA989uG8y5EkSZKUIxNWziYrWdh1gaqndsvdexZ97wu2jPDHf/9NOgJ+6TXPWsaqJEmSJBWVnd2cTbXCrsOYl84VL9hMZ0dwyz17mKzU8i5HkiRJUg4MuzmbC2MuULV0hvu7+anLtnFwosK1t+yi1mjmXZIkSZKkFWbYzdlkda6za9hdSs/euIbXv2gLdz14iH9/2z+RUsq7JEmSJEkryISVs7k5uy5QtfR2bF/H0eOzfGznXo4cn+VVzzlnUb/3lpduW+bKJEmSJC03E1bOTgxj7nXO7nL4wedu5OjxGnd+/SBr+7u5ZNto3iVJkiRJWgGG3Zw9uUCVX8VyiAjeeOkWxmdq3Hr/owz3d3Ph2FDeZUmSJElaZs7ZzdlkpU5HwEBPZ96ltK2ujg7e+tLzWD/Uw0fvfoTDk9W8S5IkSZK0zAy7OZuq1hnq7SIi8i6lrfX3dPKOV2wnCP7nzj3Um67QLEmSJLUzw27OJio199hdIaMDPbzp0q08dqzC337tYN7lSJIkSVpGht2cTVbqztddQd/zjGFeev46Pv+twzx4cDLvciRJkiQtE8NuzqYMuyvuR16wmXPW9PKJ+/adWA1bkiRJUnsx7OZssuow5pXW3dnBmy/bRqXW4K/u30czpbxLkiRJkrTEDLs5m6xkC1RpZW0a7uNHXrCZBw9O8YWHnsi7HEmSJElLzLCbM4cx5+el56/juZuH+Zt/OsCjx2byLkeSJEnSEjLs5myyUmfIsJuLiOBNl2xhsLeTj927h9m62xFJkiRJ7cKwm6NqvcFso8mwc3ZzM9DbxZU7zuXw1Cx3fv1A3uVIkiRJWiKG3RxNVuoADmPO2YVjQ7zsgnV84aEn+Pbh6bzLkSRJkrQEDLs5OjI9C8DagZ6cK9Frn7eZ0cEe/ur+fUxX63mXI0mSJOksGXZzdHCiAmQrAytfPV0dvOnSrRydnuUP7vjnvMuRJEmSdJYMuzk6MJ6F3Y3DvTlXIoDzNwzyigvX85EvPsIXvnU473IkSZIknQXDbo4en6wCsNHObmFc/j2bOH/DINd94gGmHM4sSZIklZZhN0cHxiuM9HfT192Zdylq6enq4PorX8hj4zP8/me+kXc5kiRJks6QYTdHBycqztctoBeft46f/1cXcMvde7jrwUN5lyNJkiTpDBh2c3RwosI5ztctpF+9/NlcODbI//2xL7Pb7YgkSZKk0jHs5ujgRNXObkH1dXfywbfvIAFvv+keDrXmV0uSJEkqB8NuThrNxKGpqotTFdgFY0Pc+I4dHJqs8rN/ca/770qSJEklYtjNyRNTVRrNxMYRw26RXbJtlD/96Uv4+v4JfuGj91NrNPMuSZIkSdIiGHZzcmCitcfuGufsFt2rL9rIf37DC7jrwUO8568eIKWUd0mSJEmSnkZX3gWsVgcnsjmgm+zslsJPvORcDkxUeN+dD7JxuI/3vPaivEuSJEmS9BQMuzk50dl1zm5p/NKrn8mBiQr/4x8eYrCnk2tf9UwiIu+yJEmSJJ3E0w5jjoiNEXFHRHw8ItYuuNYfEbdHxG0RcX5kbm6du6x1z/si4taIeF3r+LrW8dWt49+JiL+PiJuW4wWL6vGJCh0BG4YcxlwWEcHv/tjzecMlW7j+bx/kN277qnN4JUmSpIJazJzd64B3A+8F3rbg2juBm4BrgGuBK4B7gSuBayLi+QAppTcCV7bC8ktax98bEd3A7pTSq4HRiFg1bc4D4xXG1vTS2WFnsEw6O4L3/cTF/OKrnslf3rOXd354J1Ou0ixJkiQVzmLC7lbgq8ADwPkLrm0HdqWUDgID845rQGXuuHXv5LxnATwK9KeUboqILqAvpVRZ+OERcVVE7IyInYcOHTqNVyu2g5NuO1RWEcGv/dBz+C9vfAH/+K3DXPlnX2T/+EzeZUmSJEmaZzFzdhtAAJ3A0IJrVaC79fPIaR7PPW8C+HXgj0/24SmlG4AbAHbs2NE2y+AeHK+wbf1A3mXoJG65e8+i733by87jL+/Zww+9/y4+dvXLee7m4WWsTJIkSdJiLaazuxO4FLgE2HuyaxGxBZiZd9wP9JN1dS+ObBWfdcDDZN1egIuAwxHx/cC6lNJnzvJdSuXgZIWNw87XLbtnb1zDVd93AQBX/tkX2bXnaM4VSZIkSYLFhd2bgHeRzdu9KyLePO/ap4HLgQ8A16eUdgFjwCeA96eUDpMF5E8BN6aUGsCdEfFJ4M6U0ixwM7AhIj4UEZuW6sWKrFJrcOx4jU0OY24Lm0f6+YUfeCajg92888M72X14Ou+SJEmSpFXvaYcxp5TGgbee4loNuHrBufcsOL4euH7e8c1kAXfueNvplVx+j7f22D3HsNs2Rvq7+fD/dRlv+h9f4B1/fg+3/sIrWO9K25IkSVJuFtPZ1RKb22PXzm57uWBsiA+94yUcGK/wsx/eycxsI++SJEmSpFXLsJuDg62w62rM7efF543y337qEr667xi/9Jf3U3cfXkmSJCkXi1mNWUvsoJ3dtjR/FecrXvgMPvWVx/jpD93N6y5+BtkabSf3lpeuupH8kiRJ0rIz7Obg4ESF3q4Ohvv987erl1+wnvHjNe765iFG+rv5geeck3dJkiRJ0qriMOYcHJyosmmk7ym7fSq/f/28jbxw6wh3fv0ge48cz7scSZIkaVUx7ObgwESFjWscwtzuOiJ4w4u2MNzfzW27HqXRTHmXJEn/f3t3Hh9Xdd99/PO7s2m0W7I277uNDd5qbAgxAcreAFmbhCck5ZWU0DRNCK+SrU0gaVKatg/pA+0rDU1SMAHCK5TFYJOUh6ZQAg82iRcZcMBgY3mR5AVtlkbLzHn+uHfkkW1s2R7NaPm+X9JLc5e587vn3hnN755zzxERERkzlOzmQXNbgpoyJbtjQSwS4upFE2hsS/D8G/vyHY6IiIiIyJihZDfHnHNBza7GYB0rzqgrZcGEUp7Z2syBju58hyMiIiIiMiYo2c2xtkQfid4UtarZHVOuWjiBkGc8vnEPzqk5s4iIiIjIUFOym2PpYYeqNezQmFIaj3DZglq27etgY0NLvsMRERERERn1lOzmmMbYHbuWT69gSkUha+r3cqi7L9/hiIiIiIiMakp2c6yx1U92a0p1z+5Y45nxwSUT6e5NsbZ+b77DEREREREZ1ZTs5lhzu99BUY1qdsekmtICzp8zng0NLWxr7sh3OCIiIiIio5aS3RxrbE1QFo9QEAnlOxTJkwvmVjO+OMqjG3bR3ZvMdzgiIiIiIqOSkt0ca2pLqAnzGBcJeXx46SRaOntZu0XNmUVEREREhoKS3Rzzk101YR7rplYWsXJ2Fet3vMN/bW3KdzgiIiIiIqOOkt0ca2rrVrIrAFx8RjW1pQV89T/qeedQT77DEREREREZVcL5DmAsSaYc+zq6NeyQABAOeXx02ST+9dk3+ebjW/jna5cO+rkPvLTzlF7z2hVTTul5IiIiIiIjjWp2c+hARzfJlNM9u9KvrizOTRfP4cnNe1m9aU++wxERERERGTWU7OZQY1t6jF3V7Mphnzt/BkumlPPNx7bQFJwjIiIiIiJyepTs5lBTm8bYlaOFQx53/PFiuvuSfOXhzTjnTnlbzjl2v9NFw8FOUqexHRERERGRkU737OZQuma3tkzJrgw0fXwRX7/iDG5d/Qp/9dgWvn7FPEoKIoN+fqI3ycaGFtbvOMjeVv88K4h4zBhfzKzqYmZVFVNZHB2q8EVEREREhh0luznU3JbAM6gsUtIhR7vunKm8faCTf39hO8+81sStVy3gijNrMbN3fc7ud7pYt+MAmxpa6UmmqCsr4JrFE4hHQmxr7mDbvg5e3dsGQFk8QjTs8dFlk3O1SyIiIiIieaNkN4caWxNUlcQIh9R6XI7meca3rprP1Ysn8I1H6vn8/b/jonnVfPvqBUyuKASguy/J+u3vsGbzHn7f1MH+jm4iIWPhpHKWT6tg0rh4f3K8cFI5zjkOHuph274ONu5s4ZaHN7N+x0G+c82ZFERC+dxdEREREZEhpWQ3h5raNcaunNjiyeWs/sJ53PPCDu54+nUu/cFzXHfuVLbvP8Rvtu2nsydJ2DNmVBXxnpmVLJpUTjx67MTVzKgsjlFZHOPsaRU0tyW469fb2LyrlR9+8g+YPr4ox3snIiIiIpIbSnZzqLkt0V9DJ3I84ZDHZ1fO4Iqz6rht9Svc/dxbTCyP8+Glk7hwXhU7D3QRDZ9cCwHPjJsvncvSqeP48kMbuequ5/n7jyzkyrPqhmgvRERERETyR8luDjW2JVg2bVy+w5ARZGJ5nH/71DJaOnsoi0f6myg/8NLOU97mBXOrWfPFlfz5A7/j8/f/juvPm8bXrphHLKxmzSIiIiIyeujm0RxJ9CZp6eylVs2Y5RSUF0aP21HVyZpQHuehG87l+vOm8e+/2cEH/+UFtjW3Z237IiIiIiL5pmQ3R5qDMXarlezKMBENe9x61QJ+/KllNLUl+KM7n+e+F3ec1ji/IiIiIiLDhZLdHGlqD8bYVbIrw8zF82t46qaVnDOjkm8+/gqfufdl9rV35zssEREREZHTont2c6Sx1U921RuzHOl07r/NluqSAu65/mxWvfg231v7Glf8n+f47gfO5NL5tXhe9ppPi4iIiIjkipLdHGlqU82u5N+JEutIyOPP3jeTh9Y3cOPPfkdVcYxzZ1Zy+4fOoiimjwsRERERGTnUjDlHmtoSxMIepXElDDK81ZQW8PkLZ/LHyyYRi3is3rSHc25/hu+teZWGg535Dk9EREREZFCUeeVIU1s3tWUFWe1RV2SohD2PxZPHsWhSOQ0HO9nV0sVPf7ODnzy/nXNmVLJydhUrZ49nfl2pmjmLiIiIyLCkZDdHGtsS1JSoCbOMLGbGlMoiplQWsWBCGS9tP8DWve288OYBvv9LKIqGmFldzOzqEmZVF1MWjwx4/rUrpuQpchEREREZ65Ts5oBzjh37D3HuzMp8hyJyysriES6dX8ul82tpS/TyZnMHbzR3sK25g827WgGoLokxq7qY2dXFTB9fnOeIRURERGQsU7KbAxsaWmhu7+b82VX5DkUkK0oLIiyZMo4lU8bhnKOxLcG2IPFdt/0gL7x5gJAZ//lqI1eeVccVZ9ZSWRzLd9giIiIiMoYo2c2BtZv3EgkZF8+vyXcoIllnZtSVxakri7NydhW9yRQ7DhxiW3MHe1q6+OvHtnDr6ld476zxXLVoApctqKGkIHLiDYuIiIiInAYlu0PMOcdTWxo5f3bVUfczioxGkZDH7OoSZleX8Inlk3ltbzurN+3hiU17+MtfbOIbj3pcfEY11y6fyntmVqqDKxEREREZEkp2h9jGhhZ2t3Rx8yVz8h2KSM49uK4BgCkVhXz+gpk0HOxk065Wfr11H2vrG6ksirJiegVLp4yjMBjHV/flBm4AABCbSURBVJ1aiYiIiEg2KNkdYmvr1YRZBAb27Hz5mbVs2d3KS9sPsnZLI//5ahNnTSxjxfQKnHMaoktERERETpuS3SHknGNtfSMr1YRZZIBIyOvv4Gpvaxfrth9kQ0MLGxpaePaN/fyvFVP4wJKJFMf0ESUiIiIip8bLdwCj2aZdrexu6eLKs+ryHYrIsFVXFueaxRP5+uXzuGbxBAD++rEtrPje/+WvHq3n1T1teY5QREREREYiVZsMoXQT5kvUhFnkhGKRECumV/JPH1vMhoYWfvb/3uYXv93F/S/tZGJ5nBXTK1gxo4IV0yuZWlmops4iIiIiclxKdoeIc441m/fy3lnj1YRZ5CSkO7VaNrWC+XWlbN7Vypv7OvjVK408smE3AKUFYSZXFFIcC1MYDVMYDXHx/GrKC6MURf2PNeccLmO75YURJpTHKdWwRyIiIiJjgpLdIbI5aMJ808Wz8x2KyIhVGA1zzoxKzplRiXOOfe3dbD9wiO37D7GnpYu3upMkepM4YE393kFtMxb2KC+MUB6PUl4Y4bMrZ3D+nPHEwqGh3RkRERERySklu0Mk3YT50vm1+Q5FZFQwM6pLC6guLWDF9Mr++SnnSPQk6exJ0tnTR0/SBesPfP6h7j5au3pp6eylpauX1s4edhw4xJ+uepmSgjCXLajl/QvrOG/WeCIhdWcgIiIiMtIp2R0Czjme3LyX82aNp6xQTSZFhpJnRmEsHIzTGzup5yZTjskVcZ7YtJdfbWnk4d/uYlxhhMvPrOOqhXWsmFFJyNO9wSIiIiIjkZLdIZBuwvwlNWEWGdZCnnHB3GoumFtNd9+ZPPf6fp7YtIfHN+7mwXU7GV8c44/OquX9iybwB1PG4SnxFRERERkxlOwOgbX1ewl7xqXqhVlkxIiFQ1wyv4ZL5tfQ1ZPk179v5olNe/j5+gbuffFt6soKuGBuNfMnlHJGbQlza0soUWdXIiIiIsOWkt0sc86xpt5vwlxeGM13OCJyAg+8tPNdl62cXcXyaRW81thO/a4WHt2wiwfXpfqXTxoXZ15tKVMqCqkpjVFTWkB1+m9JjOJYWEMkiYiIiOSJkt0sq9/dyq53uvjiRWrCLDIaxCIhFk8uZ/HkcpxztHb10tiWoLE1wd7WBJt2tfDs6830Jt1Rz42GPEoKwpTGI5QUhFk+rYK68jhza/ya4aqSk7vHWEREREQGT8luFm1tbOO7a17zmzAvUBNmkdHGzCgvjFJeGGVebWn/fOcc3X0p2hN9tCV6aU/0+o+7emlL9NGe6GP3O13c37STrt5k//Mqi6LMCRLfeUHT6Dk1JRTF9NEsIiIicrr0jSoL3j5wiB88/TqPb9pDcSzMbVcvUBNmkTHEzCiIhCiIhI5bW+uc41BPkqagZripLUHDO528/PbBATXDFUVRakoLqC2NUR6PcvlZtVQWRRlXFKWyKEppQWRQnWU55+hJpujpS9GbdPT0+Y/T8wA8D0JmmBme+Z12xSMhCmNh4pGQeqMWERGREeuEya6Z1QD3Au3AnzrnWjKWxYEHAQfcDOwAVgElwN8659aZ2R3ANOAe59xqM7sFOBf4lXPuR2Z2LfAJYL1z7jvZ3Lmh1tSW4M5n3uCh9Q2EQ8bnzp/Jje+boURXRI7JzCiOhSmuKmZmVXH//JRzvHOox0+C2xI0tnXT1Jpg6942HPDoxt0DtuMZhEMeEc8IeUY45BHyDAN6g0Q20Zsi6Y5uWn2yYmGPoiDxDYeMsGdEQl7wa8HrGsEPZmCY/zfjsR+3EQllPv/wNlLOkUpB0rngsSPl/GmXuSzl2Hmwk96kI5lK0Zdy/Y+d8/8ZOedwDlKAB0TCHtGQR115AYXREPFImMJoyH8cDfnJfTREPOr/S0yl3IA4kqnDcSSDZc75Q1clU8F6zl8vlXJ4nhEL+/sWDXtEQyGiYY/CaIiiWJjimP+3MBqmOBYmFvaIhj1iYY9YJEQs7BH2TPd7i4iInKbB1OzeAnwFfwDL64C7MpZ9Bvgp8FKw3n8D64EfAj8ys04A59yHzOw+M3sOODuYXmVmdwOfds5dZmZ3mll5ZjI9nD1Vv5ebHtpIMuX4xPIp/MVFs6guLch3WCIyAnlmVBbHqCyOMX9CWf/8vmSKju4+DvUkOdTdx6HuPjp7knT29AVJ1uEEMOX8JC/k+QlpOgEOpxNizwh5fhIVCllQm0v/8/wk0U+80wlzZi1wbzIVJHp+ktfTl6Krx0/y0im1n1sH00Hi6c/3HzkOJ4jppDG9Hx5BchzUMJtZkDz7cXpB4pyufQ6HPAoioQH76h0j0e7fn6SjKBqms6ePAx09dPUm6erxfzt7kyRTJ74wkE7mPbOBsXI4Zi8o075UKiMZPrXzwoILCO/2el6wgtdfRv58MpaHQ0Y05AUXKjwiYf8iSTjjokP6AkZmawHLiCFzmuCYOOefC6lgH/tS6YT/6OObviDQl8xYJzhvPfPPz8xfz/zz1fP88zQcsoHr2eFzOOT5sfRlxNEfT8r1H4e+lBtwPFIZFylSwXmfvuCSOT9d/iFvYBl75sfn9Zf/4XPTMtcJjtuxy9QGTgd/U0dcXEm/R5NHxJdMOcz8cvC8zDLjmOWZfjygvINzKH3xJrMcjozhWMsOz88sU4Jl/kWh4x3bI5eFPAh7Xv9+pNdLG/hZk57nBi4c+HDA58/heUev6zJmDnjL9m/+8DnUm/TPrb6kf271pRx9wWfk4c/Kw1s56lj3nwsDzw1OsDzz+teR2+QEz3nX846BK77bez99rPuS6fdYqv8cTJ/v6eOY2WIo5B1refB+6V/n8HsnlN6eZ3xwyURWzq5C5HQMJtmdBNQDUfwa2EzTgMedc01mVhhMb3DO9ZpZIj0drNuesS2A3fg1wLuC6VeAKcCAZNfMbgBuCCY7zOz3g9mxXPpu8Jsl44H92dvckFGcY4/KMrtUntmjshQRGWV+kO8AJJ/mZmtDg0l2k/gXdkJA8RHLuoH0QJNlJzkdAsozYjjW9nHO3Q3cPYg4RwUze9k5tyzfcZyI4hx7VJbZpfLMHpWliIjI6GFmL2drW94g1nkZWAosARqOtczMJgJdGdNxII5fq7vI/DYVFcBb+LW9APOARiBkZt67bF9ERERERETkpA0m2f0pcBP+fbvPmdnHM5Y9CVwC/Aj4R+fcBqAKeBj4gXNuP34C+wTwE+dcEnjazB4HnnbO9eB3fvUIsM85p2RXRERERERETptl3pAv+WdmNwRNt4c1xTn2qCyzS+WZPSpLERGR0SOb/9eV7IqIiIiIiMioM5hmzCIiIiIiIiIjipJdERERERERGXWU7A4TZlZjZr80s1+YWfkwiOdcM3vKzP7VzFZkxmZmcTN7zMweNbPp5rsvmLc8hzF+ysy+NJh4zOwOM3vEzK4Opm8Jpj+Xq3iHq+D4PmtmL5rZ1JM91keW7VgXlNu9Zvbgqbx3VJ5HM7OHVZYiIiIjVza/t5vZtWb2hJl960SvO5hxdiU3bsHv8ToGXAfcld9wWAR8xTlXb2b/yMDYHH4v3S/hx/3fwHrgh/g9c68b6uDMbBFwAbAZ+Mzx4jGzTgDn3IeCN9NzwNnB9Kog5rFsDnAFfs/qG4H3MchjfWTZAqtzHfwwdDbwLaAU//xchMrzlJnZH+KX6ZGfkSpLERGRESCb39vN7G7g0865y8zsTjMrd861vNtrq2Z3+JgE1OOfBNPzHAv44x7fbP4wUYsZGNs0YINzrgkozJjuBRK5CM45twlYFUyeKJ5p+GM+A7RzuKwBdptZaS5iHq6cc/c55zqBZfgfPCdzrKcxsGzHPOfc04ABXwN+gcrzlJlZBLge/73eh8pSRERkxMnm93agBNgVTL8CTDneayvZHT6S+F+QQ0BxnmMBuMU5dz1wO3AhA2PrBiLBemXHmM61E8VzvOnhUt55ZWZzgMnAAU7uWOf72A9XB/A/gD+MyvN0fAH4Z/zPxyM/I1WWIiIiI8/pfm8v53Dr5BN+j1eyO3y8DCzFr1FtyHMsAJ8I/lbgN8nMjO1lYKmZTQS6MqbjQDwPsZ4ong3AIjMz/P15C/+qEcA8YH/OIx5GzKwQ+HvgZo4+D0+2bMc8M/sIflPbB4AUKs/TsQT4OHB58FdlKSIiMrKd7vf2RiBkZh6DyJt0z+7w8VPgX/Cr5m/IcywAiaAJcxL4MvA9Dsd2EL+25U+Av3TObTWzjwMPA3+Xh1ifPF48zrn9ZtYAPAH8xDmXNLOng/172jnXk4eYh5OvA9X4Ce//ADcxyGN9ZNnmIfbhaCt+U50EftPwW1B5nhLn3KcAzOw24HOoLEVEREa60/7ebmb3Ao8ArzrnjpvsmnNuSPdGREREREREJNfUjFlERERERERGHSW7IiIiIiIiMuoo2RUREREREZFRR8muiIjIEDCzkJkVmNl4M5tjZhea2Z+Z2fhjrFs+yG2WBj1Uvtvy84MeLjGzaHpdM/PMLHSq+yIiIjISqTdmERGRLDOz6/F7j96PPxRVDHgGaAamAvvNbAZQGjzlFjN7FlgXTLc75948YpuGP6RVPX4v6sdyOf4Yz/cD3wHONrOF+EM5/G/gV1nZQRERkRFAvTGLiIhkmZmFnXN9weP3Ahc7524LpkP4YzA/BvwMKAfOC6bTbnDOXXnENv8O2AlU4Y9N+A8u+CduZh8Dvo0//iDAAqAGf2zCrzjnbsz+XoqIiAxvSnZFRESyyMyq8BPXXvxxgZfgj7t8AHgT/xaia4F/AoqBmUAH0ASEAAMOOOc+FmwvDNwF7HTO3R7M+yrwh8C3nXO/MbOVwHzgPc65T5vZJcE2rwRmAGuccw/kYPdFRESGDSW7IiIiWWZmpcAHgt9f4tewRoE24MfOuT3Bel8ALgS+D7QDtwGfdc61B8sXAT/Er7GtADL/ae8DyvAT63uBtcH86cBX8WuLG/CbTt/onDtnSHZWRERkmFKyKyIikkVmNg74G+BxYDPwJWCVc26rmZ2LX6u7Cj+BrQTOB76A30S5E3gReMQ593sziwJVzrndwbb/BMA5d88Rr3kRMAn4KP59wBfh36P7DHAZUOuc+8jQ7bWIiMjwow6qREREsusaYA5+kjsZKAAWmlkEv2lzBD/JBb8WdgNwI3AIv4OqFcB7zOx151yPmY0zsweBHqAOwMw+iV9T/H3n3BpgB/BB4HX82tyf4yfT5fjNp2uHeJ9FRESGHSW7IiIiWRTUut4T9Lb8NHAd0ArcCnwso1OpXwZPKQfOBvqA3+I3dY7g1wwfdM5tMbM1wJPBegSP7wgSXfAT31nA88By/KbNNfj3C2/Cvw9YRERkTNE4uyIiIkPAOfcWfqJ7O/79tIXAR82sMFjlA/jNmbcBf4E/VNErwMPAx51zBzM29xR+h1ZpXwQezXitrfidX10JLMWvBX4CP9ntBZaaWTzLuygiIjKs6Z5dERGRLDIzD7gPmAC8gV9Duxa/6fIngauBLwPfwL+Xd00wdFDMObcq6En508Cdzrl1ZvZzoDr4TQUvU4c/3m4U+HPn3CYz+zHwNeD9+LW7C/GT5l1m9rfAfc6513JQBCIiIsOCkl0REZFRyMyizrmefMchIiKSL0p2RUREREREZNTRPbsiIiIiIiIy6ijZFRERERERkVFHya6IiIiIiIiMOkp2RUREREREZNRRsisiIiIiIiKjzv8HJpunQWpuhsIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "#mpl.rcParams['xtick.labelsize'] = 16\n",
    "\n",
    "# ax = plt.subplot(111)\n",
    "plt.figure(figsize=(16,8))\n",
    "# 设置刻度字体大小\n",
    "plt.xticks(fontsize=8)\n",
    "plt.yticks(fontsize=8)\n",
    "# 设置坐标字体大小\n",
    "# ax.set_xlabel(fontsize=10)\n",
    "# ax.set_ylabel(fontsize=10)\n",
    "\n",
    "\n",
    "sns.distplot(df['repayment_amount'])\n",
    "plt.title('委案金额分布图')\n",
    "plt.xlabel('委案金额')\n",
    "plt.xticks([0,5000,10000,20000,30000,40000,100000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x216de895d30>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.distplot(df[df['repayment']!=0])\n",
    "sns.distplot(df[df['repayment']!=0]['repayment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x216e01ecc18>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df['repayment_amount'])\n",
    "sns.kdeplot(df[df['repayment']!=0]['repayment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x216de556940>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['age'] = df['id'].apply(lambda x: 2019-int(x[6:10]))\n",
    "sns.kdeplot(df['age'])\n",
    "sns.kdeplot(df[df['repayment']!=0]['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86      38\n",
       "215     38\n",
       "337     38\n",
       "356     32\n",
       "527     25\n",
       "569     26\n",
       "683     31\n",
       "1166    22\n",
       "1230    22\n",
       "1563    25\n",
       "1567    41\n",
       "1751    23\n",
       "1809    40\n",
       "2168    25\n",
       "2620    25\n",
       "2690    30\n",
       "2842    38\n",
       "3243    28\n",
       "3452    38\n",
       "3650    40\n",
       "3716    22\n",
       "3862    32\n",
       "4034    34\n",
       "4364    39\n",
       "4564    35\n",
       "4692    21\n",
       "4694    41\n",
       "4701    31\n",
       "5317    23\n",
       "5424    38\n",
       "5439    29\n",
       "5515    27\n",
       "5541    31\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
